weeeeh

code/plot_eodform_spikehist.py

@@ -9,7 +9,7 @@ from IPython import embed
 inch_factor = 2.54
 data_dir = '../data'
 #dataset = '2018-11-09-ad-invivo-1'
-dataset = '2018-11-14-al-invivo-1'
+dataset = '2018-11-13-aa-invivo-1'
 
 # read eod and time of baseline
 time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
@@ -29,7 +29,6 @@ ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
 fig.tight_layout()
 plt.show()
-plt.show()
 #plt.savefig('isis.pdf')
 exit()
 plt.savefig('isis.png')

code/response_beat.py

@@ -13,7 +13,7 @@ cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
 window = 1
 
 # norm: -150, 150, 300  aa, #ac, aj??
-data = ["2018-11-13-al-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
+data = ["2018-11-13-aa-invivo-1"]#, "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
         #"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
 
 '''
@@ -67,10 +67,10 @@ for dataset in data:
                 binary_spikes = np.isin(cut_range, spikes_idx) * 1
                 smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
                 train = smoothed_data[window:beat_window+window]
-                norm_train = train*1000#/spikerate
+                norm_train = train*1000/spikerate
                 rep_rates.append(np.std(norm_train))#/spikerate)
                 break
-        df_rate = np.median(rep_rates)/spikerate
+        df_rate = np.mean(rep_rates)
         #embed()
         #exit()
         if df in rates.keys():

code/spikes_analysis.py

@@ -8,7 +8,8 @@ from IPython import embed
 # define sampling rate and data path
 sampling_rate = 40 #kHz
 data_dir = "../data"
-#dataset = "2018-11-13-al-invivo-1"
+dataset = "2018-11-13-ah-invivo-1"
+
 '''
 data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1",
     "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
@@ -18,9 +19,10 @@ data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-inviv
     "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1",
     "2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
     "2018-11-20-ai-invivo-1"]
-'''
+
 data = ["2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1",
     "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
+'''
 
 # parameters for binning, smoothing and plotting
 cut_window = 20
@@ -53,7 +55,7 @@ df_phase_binary = {}
 #embed()
 #exit()
 
-for dataset in data:
+#for dataset in data:
 spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
 df_map = map_keys(spikes)
 print(dataset)
@@ -168,17 +170,20 @@ for dataset in data:
         plt.show()
         '''
 
-    '''
+upper_limit = np.max(sorted(csi_rates.keys()))+30
+lower_limit = np.min(sorted(csi_rates.keys()))-30
+
 fig, ax = plt.subplots()
 for i, k in enumerate(sorted(csi_rates.keys())):
-        ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
+    ax.scatter(np.ones(len(csi_rates[k]))*k, csi_rates[k], s=20)
     #ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
-    ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
+#ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
-    ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
+ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
 #ax.set_xticklabels(sorted(csi_rates.keys()))
 fig.tight_layout()
 plt.show()
 
+'''
 fig, ax = plt.subplots()
 for i, k in enumerate(sorted(csi_trains.keys())):
     ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
@@ -190,6 +195,7 @@ for dataset in data:
 plt.show()
 '''
 
+'''
 fig, ax = plt.subplots()
 for i, k in enumerate(sorted(beat.keys())):
     ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
@@ -197,3 +203,4 @@ for dataset in data:
 #ax.set_xticklabels(sorted(csi_trains.keys()))
 fig.tight_layout()
 plt.show()
+'''