Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio
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commit
509be03d92
@ -7,31 +7,45 @@ from IPython import embed
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# plot and data values
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inch_factor = 2.54
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sampling_rate = 40000
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data_dir = '../data'
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#dataset = '2018-11-09-ad-invivo-1'
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dataset = '2018-11-14-al-invivo-1'
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dataset = '2018-11-09-ad-invivo-1'
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#dataset = '2018-11-13-aa-invivo-1'
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# read eod and time of baseline
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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eod_norm = eod - np.mean(eod)
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# calculate eod times and indices by zero crossings
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threshold = 0
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shift_eod = np.roll(eod_norm, 1)
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eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
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eod_duration = eod_times[2]- eod_times[1] #time in s
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eod_duration = eod_times[2]- eod_times[1]
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# read spikes during baseline activity
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
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# calculate interpike intervals and plot them
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interspikeintervals = np.diff(spikes)
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interspikeintervals = np.diff(spikes)/eod_duration
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='royalblue')
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plt.xlabel("time [ms]", fontsize = 22)
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.1), color='royalblue')
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plt.xlabel("eod cycles", fontsize = 22)
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plt.xticks(fontsize = 18)
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plt.ylabel("number of \n interspikeintervals", fontsize = 22)
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plt.yticks(fontsize = 18)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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fig.tight_layout()
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plt.show()
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plt.show()
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#plt.show()
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#plt.savefig('isis.pdf')
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exit()
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plt.savefig('isis.png')
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@ -85,10 +99,10 @@ plt.yticks(fontsize=18)
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ax1.spines['top'].set_visible(False)
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ax2 = ax1.twinx()
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
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ax2.plot(time_axis, mu_eod, color='black', lw=2)
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ax2.set_ylabel('voltage [mV]', fontsize=22)
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ax2.tick_params(axis='y', labelcolor='navy')
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ax2.tick_params(axis='y', labelcolor='royalblue')
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ax2.spines['top'].set_visible(False)
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plt.yticks(fontsize=18)
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@ -54,16 +54,17 @@ for deltaf in df_map.keys():
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# get spikes between 60 ms before and after the chirp
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spikes_to_cut = np.asarray(spikes[rep][phase])
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spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window) & (spikes_to_cut < cut_window)]
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spikes_raster = spikes_to_cut[(spikes_to_cut > -cut_window+5) & (spikes_to_cut < cut_window-5)]
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spikes_idx = np.round(spikes_cut*sampling_rate)
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# also save as binary, 0 no spike, 1 spike
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binary_spikes = np.isin(cut_range, spikes_idx)*1
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# add the spikes to the dictionaries with the correct df and phase
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if idx in df_phase_time[deltaf].keys():
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df_phase_time[deltaf][idx].append(spikes_cut)
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df_phase_time[deltaf][idx].append(spikes_raster)
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df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes))
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else:
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df_phase_time[deltaf][idx] = [spikes_cut]
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df_phase_time[deltaf][idx] = [spikes_raster]
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df_phase_binary[deltaf][idx] = binary_spikes
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@ -80,15 +81,13 @@ for df in df_phase_time.keys():
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smoothed_spikes = smooth(plot_trials_binary, window, 1./sampling_rate)
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fig, ax = plt.subplots(2, 1, sharex=True, figsize=(20/inch_factor, 15/inch_factor))
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fig, ax = plt.subplots(2, 1, sharex=True, figsize=(18/inch_factor, 13/inch_factor))
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for i, trial in enumerate(plot_trials):
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ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k')
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ax[1].plot(time_axis, smoothed_spikes*1000, color='royalblue', lw = 2)
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ax[1].plot(time_axis[0+5*sampling_rate:-5*sampling_rate], smoothed_spikes[0+5*sampling_rate:-5*sampling_rate]*1000, color='royalblue', lw = 2)
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ax[0].set_title('df = %s Hz' %(df))
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ax[0].set_title('df = %s Hz' %(df), fontsize = 18)
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ax[0].set_ylabel('repetition', fontsize=22)
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ax[0].yaxis.set_label_coords(-0.1, 0.5)
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ax[0].set_yticks(np.arange(1, len(plot_trials)+1,2))
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@ -13,7 +13,7 @@ cut_range = np.arange(-cut_window * sampling_rate, 0, 1)
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window = 1
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# norm: -150, 150, 300 aa, #ac, aj??
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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",
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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",
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#"2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
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'''
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@ -67,10 +67,10 @@ for dataset in data:
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binary_spikes = np.isin(cut_range, spikes_idx) * 1
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smoothed_data = smooth(binary_spikes, window, 1 / sampling_rate)
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train = smoothed_data[window:beat_window+window]
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norm_train = train*1000#/spikerate
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norm_train = train*1000/spikerate
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rep_rates.append(np.std(norm_train))#/spikerate)
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break
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df_rate = np.median(rep_rates)/spikerate
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df_rate = np.mean(rep_rates)
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#embed()
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#exit()
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if df in rates.keys():
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@ -8,7 +8,8 @@ from IPython import embed
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# define sampling rate and data path
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sampling_rate = 40 #kHz
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data_dir = "../data"
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#dataset = "2018-11-13-al-invivo-1"
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dataset = "2018-11-13-ah-invivo-1"
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'''
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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",
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"2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1",
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@ -18,9 +19,10 @@ data = ["2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-inviv
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"2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1",
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"2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1",
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"2018-11-20-ai-invivo-1"]
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'''
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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",
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"2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1"]
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'''
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# parameters for binning, smoothing and plotting
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cut_window = 20
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@ -53,12 +55,12 @@ df_phase_binary = {}
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#embed()
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#exit()
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for dataset in data:
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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print(dataset)
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# iterate over delta f, repetition, phases and a single chirp
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for deltaf in df_map.keys():
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#for dataset in data:
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spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(spikes)
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print(dataset)
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# iterate over delta f, repetition, phases and a single chirp
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for deltaf in df_map.keys():
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df_phase_time[deltaf] = {}
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df_phase_binary[deltaf] = {}
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for rep in df_map[deltaf]:
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@ -87,12 +89,12 @@ for dataset in data:
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df_phase_binary[deltaf][idx] = binary_spikes
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# make dictionaries for csi and beat
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csi_trains = {}
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csi_rates = {}
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beat = {}
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# for plotting and calculating iterate over delta f and phases
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for df in df_phase_time.keys():
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# make dictionaries for csi and beat
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csi_trains = {}
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csi_rates = {}
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beat = {}
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# for plotting and calculating iterate over delta f and phases
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for df in df_phase_time.keys():
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csi_trains[df] = []
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csi_rates[df] = []
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beat[df] = []
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@ -168,32 +170,37 @@ for dataset in data:
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plt.show()
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'''
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_rates.keys())):
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ax.scatter(np.ones(len(csi_rates[k]))*i, csi_rates[k], s=20)
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upper_limit = np.max(sorted(csi_rates.keys()))+30
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lower_limit = np.min(sorted(csi_rates.keys()))-30
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_rates.keys())):
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ax.scatter(np.ones(len(csi_rates[k]))*k, csi_rates[k], s=20)
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#ax.plot(i, np.mean(csi_rates[k]), 'o', markersize=15)
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ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot(np.arange(-1, len(csi_rates.keys())+1), np.zeros(len(csi_rates.keys())+2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_rates.keys()))
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fig.tight_layout()
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plt.show()
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#ax.legend(sorted(csi_rates.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot([lower_limit, upper_limit], np.zeros(2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_rates.keys()))
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fig.tight_layout()
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plt.show()
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_trains.keys())):
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(csi_trains.keys())):
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ax.plot(np.ones(len(csi_trains[k]))*i, csi_trains[k], 'o')
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#ax.plot(i, np.mean(csi_trains[k]), 'o', markersize=15)
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ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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'''
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ax.legend(sorted(csi_trains.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys())+2), 'silver', linewidth=2, linestyle='--')
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(beat.keys())):
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'''
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fig, ax = plt.subplots()
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for i, k in enumerate(sorted(beat.keys())):
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ax.plot(np.ones(len(beat[k]))*i, beat[k], 'o')
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ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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ax.legend(sorted(beat.keys()), loc='upper left', bbox_to_anchor=(1.04, 1))
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#ax.set_xticklabels(sorted(csi_trains.keys()))
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fig.tight_layout()
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plt.show()
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'''
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@ -35,13 +35,13 @@ for k, t in enumerate(time):
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p += f * stepsize
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signal[k] = a * np.sin(6.28318530717959 * p)
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fig = plt.figure(figsize = (20/inch_factor, 15/inch_factor))
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fig = plt.figure(figsize = (20/inch_factor, 12/inch_factor))
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ax1 = fig.add_subplot(211)
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plt.yticks(fontsize=18)
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ax2 = fig.add_subplot(212, sharex=ax1)
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plt.setp(ax1.get_xticklabels(), visible=False)
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ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
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ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
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ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
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ax1.set_ylabel("field [mV]", fontsize = 22)
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