diff --git a/code/spikes_analysis.py b/code/spikes_analysis.py index 4ebdb0b..66ac833 100644 --- a/code/spikes_analysis.py +++ b/code/spikes_analysis.py @@ -8,7 +8,7 @@ from IPython import embed # define sampling rate and data path sampling_rate = 40 #kHz data_dir = "../data" -dataset = "2018-11-14-aa-invivo-1" +dataset = "2018-11-14-ad-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", \ # "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", \ @@ -18,28 +18,29 @@ dataset = "2018-11-14-aa-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") # parameters for binning, smoothing and plotting -#cut_window = 60 cut_window = 20 -chirp_size = 14 #ms +#cut_window_csi = 20 #ms +#cut_window_plot = 50 #ms +chirp_duration = 14 #ms neuronal_delay = 5 #ms -chirp_start = int((-chirp_size/2+neuronal_delay+cut_window)*sampling_rate) -chirp_end = int((chirp_size/2+neuronal_delay+cut_window)*sampling_rate) -num_bin = 12 +chirp_start = int((-chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index +chirp_end = int((chirp_duration / 2 + neuronal_delay + cut_window * 2) * sampling_rate) #index +number_bins = 12 window = 1 #ms -time_axis = np.arange(-cut_window, cut_window, 1/sampling_rate) #steps -spike_bins = np.arange(-cut_window, cut_window+1) #ms +time_axis = np.arange(-cut_window*2, cut_window*2, 1/sampling_rate) #steps +spike_bins = np.arange(-cut_window*2, cut_window*2) #ms # read data from files spikes = read_chirp_spikes(os.path.join(data_dir, dataset)) -eod = read_chirp_eod(os.path.join(data_dir, dataset)) -chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) +#eod = read_chirp_eod(os.path.join(data_dir, dataset)) +#chirp_times = read_chirp_times(os.path.join(data_dir, dataset)) # make a delta f map for the quite more complicated keys df_map = map_keys(spikes) # differentiate between phases -phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin) -cut_range = np.arange(-cut_window*sampling_rate, cut_window*sampling_rate, 1) +phase_vec = np.arange(0, 1 + 1 / number_bins, 1 / number_bins) +cut_range = np.arange(-cut_window*2*sampling_rate, cut_window*2*sampling_rate, 1) # make dictionaries for spiketimes df_phase_time = {} @@ -52,14 +53,18 @@ for deltaf in df_map.keys(): df_phase_time[deltaf] = {} df_phase_binary[deltaf] = {} for rep in df_map[deltaf]: + chirp_size = int(rep[-1].strip('Hz')) + #print(chirp_size) + if chirp_size == 150: + continue for phase in spikes[rep]: - for idx in np.arange(num_bin): + for idx in np.arange(number_bins): # check the phase if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: # get spikes between 50 ms before and after the chirp spikes_to_cut = np.asarray(spikes[rep][phase]) - spikes_cut = spikes_to_cut[(spikes_to_cut > -50) & (spikes_to_cut < 50)] + spikes_cut = spikes_to_cut[(spikes_to_cut > -cut_window*2) & (spikes_to_cut < cut_window*2)] spikes_idx = np.round(spikes_cut*sampling_rate) # also save as binary, 0 no spike, 1 spike binary_spikes = np.isin(cut_range, spikes_idx)*1 @@ -80,27 +85,32 @@ csi_rates = {} for df in df_phase_time.keys(): csi_trains[df] = [] csi_rates[df] = [] - beat_duration = int(abs(1/df*1000)) #ms + beat_duration = int(abs(1/df*1000)*sampling_rate) #steps beat_window = 0 - while beat_window+beat_duration <= cut_window: + # beat window is at most 20 ms long, multiples of beat_duration + while beat_window+beat_duration <= cut_window*sampling_rate: beat_window = beat_window+beat_duration for phase in df_phase_time[df].keys(): # csi calculation - # trains for synchronity and rate - + # trains for synchrony and rate trials_binary = df_phase_binary[df][phase] train_chirp = [] train_beat = [] - spikerate_chirp = np.zeros(len(trials_binary)) - spikerate_beat = np.zeros(len(trials_binary)) + #csi_spikerate = [] for i, trial in enumerate(trials_binary): smoothed_trial = smooth(trial, window, 1/sampling_rate) train_chirp.append(smoothed_trial[chirp_start:chirp_end]) train_beat.append(smoothed_trial[chirp_start-beat_window:chirp_start]) - spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end]) - spikerate_beat[i] = np.mean(smoothed_trial[chirp_start-beat_window:chirp_start]) + #std_chirp = np.std(smoothed_trial[chirp_start:chirp_end]) + #std_beat = np.std(smoothed_trial[chirp_start-beat_window:chirp_start]) + #csi = (std_chirp - std_beat)/(std_chirp + std_beat) + #csi_spikerate.append(csi) + + std_chirp = np.std(np.mean(train_chirp, axis=0)) + std_beat = np.std(np.mean(train_beat, axis=0)) + csi_spikerate = (std_chirp - std_beat) / (std_chirp + std_beat) rcs = [] rbs = [] @@ -116,20 +126,12 @@ for df in df_phase_time.keys(): r_train_chirp = np.mean(rcs) r_train_beat = np.mean(rbs) - embed() - exit() + csi_train = (r_train_chirp - r_train_beat) / (r_train_chirp + r_train_beat) - csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat)) # add the csi to the dictionaries with the correct df and phase - #csi_trains[df][phase] = csi_train - #csi_rates[df][phase] = csi_rate - csi_trains[df].append(csi_train) - csi_rates[df].append(csi_rate) - - #csi_trains[df].append(abs(csi_train)) - #csi_rates[df].append(abs(csi_rate)) + csi_rates[df].append(np.mean(csi_spikerate)) ''' # plot @@ -173,3 +175,13 @@ ax.plot(np.arange(-1, len(csi_trains.keys())+1), np.zeros(len(csi_trains.keys()) #ax.set_xticklabels(sorted(csi_trains.keys())) fig.tight_layout() plt.show() + +# spikerate_chirp = np.zeros(len(trials_binary)) +# spikerate_beat = np.zeros(len(trials_binary)) +# csi_trains[df][phase] = csi_train +# csi_rates[df][phase] = csi_rate +# csi_trains[df].append(abs(csi_train)) +# csi_rates[df].append(abs(csi_rate)) +#csi_rate = (np.std(spikerate_chirp) - np.std(spikerate_beat)) / (np.std(spikerate_chirp) + np.std(spikerate_beat)) +# spikerate_chirp[i] = np.mean(smoothed_trial[chirp_start:chirp_end]) +# spikerate_beat[i] = np.mean(smoothed_trial[chirp_start-beat_window:chirp_start]) \ No newline at end of file diff --git a/code/stimulus_chirp.py b/code/stimulus_chirp.py new file mode 100644 index 0000000..ea785e0 --- /dev/null +++ b/code/stimulus_chirp.py @@ -0,0 +1,47 @@ +import numpy as np +import matplotlib.pyplot as plt + +stimulusrate = 500. # the eod frequency of the fake fish +currentchirptimes = [0.1] +chirpwidth = 0.05 # ms +chirpsize = 100. +chirpampl = 0.02 +chirpkurtosis = 1. +p = 0. +stepsize = 0.00001 + +time = np.arange(0.0, 0.2, stepsize) +signal = np.zeros(time.shape) +ampl = np.ones(time.shape) +freq = np.ones(time.shape) + +ck = 0 +csig = 0.5 * chirpwidth / np.power(2.0*np.log(10.0), 0.5/chirpkurtosis) + +for k, t in enumerate(time): + a = 1. + f = stimulusrate + if ck < len(currentchirptimes): + if np.abs(t - currentchirptimes[ck]) < 2.0 * chirpwidth: + x = t - currentchirptimes[ck] + g = np.exp(-0.5 * (x/csig)**2) + f = chirpsize * g + stimulusrate + a *= 1.0 - chirpampl * g + elif t > currentchirptimes[ck] + 2.0 * chirpwidth: + ck += 1 + freq[k] = f + ampl[k] = a + p += f * stepsize + signal[k] = a * np.sin(6.28318530717959 * p) + +fig = plt.figure() +ax1 = fig.add_subplot(211) +ax2 = fig.add_subplot(212) + +ax1.plot(time, signal) +ax2.plot(time, freq) + +ax1.set_ylabel("fake fish field [rel]") +ax2.set_xlabel("time [s]") +ax2.set_ylabel("frequency [Hz]") +plt.show()