diff --git a/code/read_chirp_data.py b/code/read_chirp_data.py index c3afe76..4491779 100644 --- a/code/read_chirp_data.py +++ b/code/read_chirp_data.py @@ -1,5 +1,7 @@ import numpy as np import os +import nixio as nix +from IPython import embed def read_chirp_spikes(dataset): @@ -85,10 +87,37 @@ def read_chirp_times(dataset): return chirp_times +def read_chirp_stimulus(dataset): + base = dataset.split(os.path.sep)[-1] + ".nix" + nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly) + b = nix_file.blocks[0] + data = {} + for t in b.tags: + if "Chirps" in t.name: + stims = [] + index = int(t.name.split("_")[-1]) + df = t.metadata["RePro-Info"]["settings"]["deltaf"] + cs = t.metadata["RePro-Info"]["settings"]["chirpsize"] + stim_da = t.references["GlobalEFieldStimulus"] + si = stim_da.dimensions[0].sampling_interval + for mt in b.multi_tags: + if mt.positions[0] >= t.position[0] and \ + mt.positions[0] < (t.position[0] + t.extent[0]): + break + for i in range(len(mt.positions)): + start_index = int(mt.positions[i] / si) + end_index = int((mt.positions[i] + mt.extents[i]) / si) - 1 + stim = stim_da[start_index:end_index] + time = stim_da.dimensions[0].axis(len(stim)) + mt.positions[i] + stims.append((time, stim)) + data[(index, df, cs)] = stims + nix_file.close() + return data + if __name__ == "__main__": data_dir = "../data" - dataset = "2018-11-09-ad-invivo-1" - spikes = load_chirp_spikes(os.path.join(data_dir, dataset)) - chirp_times = load_chirp_times(os.path.join(data_dir, dataset)) - chirp_eod = load_chirp_eod(os.path.join(data_dir, dataset)) - + dataset = "2018-11-20-ad-invivo-1" + #spikes = load_chirp_spikes(os.path.join(data_dir, dataset)) + #chirp_times = load_chirp_times(os.path.join(data_dir, dataset)) + #chirp_eod = load_chirp_eod(os.path.join(data_dir, dataset)) + stim = read_chirp_stimulus(os.path.join(data_dir, dataset)) diff --git a/code/spikes_analysis.py b/code/spikes_analysis.py index a665a80..2d4a329 100644 --- a/code/spikes_analysis.py +++ b/code/spikes_analysis.py @@ -4,14 +4,21 @@ from read_chirp_data import * from utility import * from IPython import embed +# define sampling rate and data path sampling_rate = 40 #kHz data_dir = "../data" dataset = "2018-11-09-ad-invivo-1" +# parameters for binning, smoothing and plotting +num_bin = 12 +window = sampling_rate +time_axis = np.arange(-50, 50, 1/sampling_rate) +# 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)) +# make a delta f map for the quite more complicated keys df_map = {} for k in spikes.keys(): df = k[1] @@ -20,61 +27,55 @@ for k in spikes.keys(): else: df_map[df] = [k] -# make phases together, 12 phases -phase_vec = np.arange(0, 1+1/12, 1/12) +# differentiate between phases +phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin) cut_range = np.arange(-50*sampling_rate, 50*sampling_rate, 1) +# make dictionaries for spiketimes df_phase_time = {} df_phase_binary = {} +# iterate over delta f, repetition, phases and a single chirp for deltaf in df_map.keys(): df_phase_time[deltaf] = {} df_phase_binary[deltaf] = {} for rep in df_map[deltaf]: for phase in spikes[rep]: - #print(phase) - for idx in range(len(phase_vec)-1): + for idx in np.arange(num_bin): + # check the phase if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]: + # get spikes between 50 ms befor 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_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 - if phase_vec[idx] in df_phase_time[deltaf].keys(): - df_phase_time[deltaf][phase_vec[idx]].append(spikes[rep][phase]) - df_phase_binary[deltaf][phase_vec[idx]] = np.vstack((df_phase_binary[deltaf][phase_vec[idx]], binary_spikes)) + # add the spikes to the dictionaries with the correct df and phase + if idx in df_phase_time[deltaf].keys(): + df_phase_time[deltaf][idx].append(spikes_cut) + df_phase_binary[deltaf][idx] = np.vstack((df_phase_binary[deltaf][idx], binary_spikes)) else: - df_phase_time[deltaf][phase_vec[idx]] = [spikes[rep][phase]] - df_phase_binary[deltaf][phase_vec[idx]] = binary_spikes + df_phase_time[deltaf][idx] = [spikes_cut] + df_phase_binary[deltaf][idx] = binary_spikes +# for plotting iterate over delta f and phases +for df in df_phase_time.keys(): + for phase in df_phase_time[df].keys(): + plot_trials = df_phase_time[df][phase] + plot_trials_binary = np.mean(df_phase_binary[df][phase], axis=0) -plot_trials = df_phase_binary['-50Hz'][0.0] -#hist_data = plt.hist(plot_trials) -#ax.plot(hist_data[1][:-1], hist_data[0]) + smoothed_spikes = smooth(plot_trials_binary, window) -fig, ax = plt.subplots() -for i, trial in enumerate(plot_trials): - embed() - exit() - trial[trial == 0] = np.nan - ax.scatter(np.ones(len(trial)), trial, marker='|', color='k', size=12) -plt.show() + fig, ax = plt.subplots(2, 1) + for i, trial in enumerate(plot_trials): + ax[0].scatter(trial, np.ones(len(trial))+i, marker='|', color='k') + ax[1].plot(time_axis, smoothed_spikes) + ax[0].set_title(df) + ax[0].set_ylabel('repetition', fontsize=12) -#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)) -#ax.plot(hist_data[1][:-1], hist_data[0]) \ No newline at end of file + ax[1].set_xlabel('time [ms]', fontsize=12) + ax[1].set_ylabel('firing rate [?]', fontsize=12) + plt.show() diff --git a/code/utility.py b/code/utility.py index 2a3f083..41aacf1 100644 --- a/code/utility.py +++ b/code/utility.py @@ -1,4 +1,5 @@ import numpy as np +from IPython import embed def zero_crossing(eod, time): @@ -23,6 +24,17 @@ def gaussian(x, mu, sig): y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.))) return y + +def smooth(data, window): + mu = 1 + sigma = window + time_gauss = np.arange(-4 * sigma, 4 * sigma, 1) + gauss = gaussian(time_gauss, mu, sigma) + gauss_norm = gauss/(np.sum(gauss)/len(gauss)) + smoothed_data = np.convolve(data, gauss_norm, 'same') + return smoothed_data + + def map_keys(input): df_map = {} for k in input.keys():