From 9bb8d66478a1f99a2b7b0b5b2d77879c76f1eef8 Mon Sep 17 00:00:00 2001
From: Ramona <efish@verliernix.com>
Date: Thu, 22 Nov 2018 16:59:47 +0100
Subject: [PATCH] crazy analysis stuff

---
 code/spikes_analysis.py | 62 +++++++++++++++++++++++++++--------------
 1 file changed, 41 insertions(+), 21 deletions(-)

diff --git a/code/spikes_analysis.py b/code/spikes_analysis.py
index b96c6cf..df95c5e 100644
--- a/code/spikes_analysis.py
+++ b/code/spikes_analysis.py
@@ -9,11 +9,14 @@ sampling_rate = 40 #kHz
 data_dir = "../data"
 dataset = "2018-11-09-ad-invivo-1"
 # parameters for binning, smoothing and plotting
+chirp_size = 14 #ms
+neuronal_delay = 5 #ms
+chirp_start = int((-chirp_size/2+neuronal_delay+50)*sampling_rate)
+chirp_end = int((chirp_size/2+neuronal_delay+51)*sampling_rate)
 num_bin = 12
-window = 1
-time_axis = np.arange(-50, 50, 1/sampling_rate)
-bin_size = 1
-spike_bins = np.arange(-50, 50+bin_size, bin_size)
+window = 1 #ms
+time_axis = np.arange(-50, 50, 1/sampling_rate) #steps
+spike_bins = np.arange(-50, 51) #ms
 
 # read data from files
 spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
@@ -66,28 +69,33 @@ for deltaf in df_map.keys():
 for df in df_phase_time.keys():
     for phase in df_phase_time[df].keys():
 
-        # plot
-        plot_trials = df_phase_time[df][phase]
-        plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
+        trials_binary = df_phase_binary[df][phase]
 
-        # calculation
-        overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
+        sr_chirp = np.zeros(len(trials_binary))
+        sr_beat = np.zeros(len(trials_binary))
+        for i, trial in enumerate(trials_binary):
+            smoothed_trial = smooth(trial, window, 1/sampling_rate)
+            sr_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end])
+            sr_beat[i] = np.mean(smoothed_trial[0:chirp_start])
 
-        '''
-        spike_rate = np.zeros(len(spike_bins)-1)
-        for idx in range(len(spike_bins)-1):
-            bin_start = spike_bins[idx]*sampling_rate
-            bin_end = spike_bins[idx+1]*sampling_rate
-            spike_rate[idx] = np.sum(plot_trials_binary[bin_start:bin_end])/bin_size*sampling_rate
-
-        print(np.std(spike_rate))
-        plt.plot(spike_rate)
-        plt.show()
+        for rate_chirp in sr_chirp:
+            for rate_beat in sr_beat:
+                r = np.corrcoef(rate_chirp, rate_beat)
+                print(r)
 
         embed()
         exit()
-        '''
 
+        #csi = (spikerate_chirp-spikerate_befor)/(spikerate_chirp+spikerate_befor)
+
+        # plot
+        #plot_trials = df_phase_time[df][phase]
+        #plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0)
+
+        # calculation
+        #overall_spikerate = (np.sum(plot_trials_binary)/len(plot_trials_binary))*sampling_rate*1000
+
+        '''
         smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
 
         fig, ax = plt.subplots(2, 1, sharex=True)
@@ -100,5 +108,17 @@ for df in df_phase_time.keys():
 
         ax[1].set_xlabel('time [ms]', fontsize=12)
         ax[1].set_ylabel('firing rate [Hz]', fontsize=12)
-        print(overall_spikerate)
         plt.show()
+        '''
+
+
+'''
+for trial in range(len(trials_binary)):
+spike_rate = np.zeros(len(spike_bins)-1)
+for idx in range(len(spike_bins)-1):
+bin_start = spike_bins[idx]*sampling_rate
+bin_end = spike_bins[idx+1]*sampling_rate
+spike_rate[idx] = np.sum(trials_binary[trial][bin_start:bin_end])
+embed()
+exit()
+'''
\ No newline at end of file