From 454ac6984921d4f10a93b047add8035f7fcede2f Mon Sep 17 00:00:00 2001 From: Ramona Date: Mon, 19 Nov 2018 16:06:57 +0100 Subject: [PATCH] phases --- code/spikes_analysis.py | 45 +++++++++++++++++++++++++++-------------- 1 file changed, 30 insertions(+), 15 deletions(-) diff --git a/code/spikes_analysis.py b/code/spikes_analysis.py index 5ae3da4..e059c38 100644 --- a/code/spikes_analysis.py +++ b/code/spikes_analysis.py @@ -9,7 +9,7 @@ dataset = "2018-11-09-ad-invivo-1" spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) eod = read_chirp_eod(os.path.join(data_dir, dataset)) -times = read_chirp_times(os.path.join(data_dir, dataset)) +chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) df_map = {} for k in spikes.keys(): @@ -20,31 +20,46 @@ for k in spikes.keys(): df_map[df] = [k] # make phases together, 12 phases -spikes_mat = {} +phase_vec = np.arange(0, 1+1/12, 1/12) +phase_mat_df = {} + for deltaf in df_map.keys(): + phase_df = {} for rep in df_map[deltaf]: for phase in spikes[rep]: #print(phase) - spikes_one_chirp = spikes[rep][phase] - if deltaf == '-50Hz' and phase == (9, 0.54): - spikes_mat[deltaf, rep, phase] = spikes_one_chirp + for idx in range(len(phase_vec)-1): + if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: + if phase_vec[idx] in phase_df.keys(): + phase_df[phase_vec[idx]].append(phase[1]) + else: + phase_df[phase_vec[idx]] = [phase[1]] -plot_spikes = spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)] + #spikes_one_chirp = spikes[rep][phase] -mu = 1 -sigma = 1 -time_gauss = np.arange(-4, 4, 1) -gauss = gaussian(time_gauss, mu, sigma) -# spikes during time vec (00010000001)? -smoothed_spikes = np.convolve(plot_spikes, gauss, 'same') -window = np.mean(np.diff(plot_spikes)) -time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window) + + phase_mat_df[deltaf] = phase_df + +embed() +exit() +plot_spikes = spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)] fig, ax = plt.subplots() ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k') -ax.plot(time_vec, smoothed_spikes) plt.show() +#mu = 1 +#sigma = 1 +#time_gauss = np.arange(-4, 4, 1) +#gauss = gaussian(time_gauss, mu, sigma) +# spikes during time vec (00010000001)? + +#smoothed_spikes = np.convolve(plot_spikes, gauss, 'same') +#window = np.mean(np.diff(plot_spikes)) +#time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window) + +#ax.plot(time_vec, smoothed_spikes) + #embed() #exit() #hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))