from stimuli.SinusAmplitudeModulation import SinusAmplitudeModulationStimulus as SAM from Baseline import get_baseline_class from FiCurve import FICurveModel from models.LIFACnoise import LifacNoiseModel import numpy as np import matplotlib.pyplot as plt import helperFunctions as hF from CellData import CellData def main(): # 2012-07-12-ag-invivo-1 fit and eod frequency: # parameters = {'refractory_period': 0.00080122694889117, 'v_base': 0, 'v_zero': 0, 'a_zero': 20, 'step_size': 5e-05, # 'delta_a': 0.23628384937392385, 'threshold': 1, 'input_scaling': 100.66894113671654, # 'mem_tau': 0.012388673630113763, 'tau_a': 0.09106579031822526, 'v_offset': -6.25, # 'noise_strength': 0.0404417932620334, 'dend_tau': 0.00122153436141022} # cell_data = CellData("./data/2012-07-12-ag-invivo-1/") parameters = {'delta_a': 0.08820130374685671, 'refractory_period': 0.0006, 'a_zero': 15, 'step_size': 5e-05, 'v_base': 0, 'noise_strength': 0.03622523883042496, 'v_zero': 0, 'threshold': 1, 'input_scaling': 77.75367190909581, 'tau_a': 0.07623731247799118, 'v_offset': -10.546875, 'mem_tau': 0.008741976196676469, 'dend_tau': 0.0012058986118892773} cell_data = CellData("./data/2012-12-13-an-invivo-1/") eod_freq = cell_data.get_eod_frequency() model = LifacNoiseModel(parameters) # base_cell = get_baseline_class(cell_data) # base_model = get_baseline_class(model, cell_data.get_eod_frequency()) # isis_cell = np.array(base_cell.get_interspike_intervals()) * 1000 # isi_model = np.array(base_model.get_interspike_intervals()) * 1000 # bins = np.arange(0, 20, 0.1) # plt.hist(isi_model, bins=bins, alpha=0.5) # plt.hist(isis_cell, bins=bins, alpha=0.5) # plt.show() # plt.close() # ficurve = FICurveModel(model, np.arange(-1, 1.1, 0.1), eod_freq) # # ficurve.plot_fi_curve() durations = cell_data.get_sam_durations() u_durations = np.unique(durations) mean_duration = np.mean(durations) contrasts = cell_data.get_sam_contrasts() contrast = contrasts[0] # are all the same in this test case spiketimes = cell_data.get_sam_spiketimes() delta_freqs = cell_data.get_sam_delta_frequencies() step_size = cell_data.get_sampling_interval() spikes_dictionary = {} for i, m_freq in enumerate(delta_freqs): if m_freq in spikes_dictionary: spikes_dictionary[m_freq].append(spiketimes[i]) else: spikes_dictionary[m_freq] = [spiketimes[i]] for m_freq in sorted(spikes_dictionary.keys()): if mean_duration < 2*1/float(m_freq): continue stimulus = SAM(eod_freq, contrast/100, m_freq) v1, spikes_model = model.simulate_fast(stimulus, mean_duration*4) prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size) # plt.plot(prob_density_function_model) # plt.show() # plt.close() fig, axes = plt.subplots(1, 4) cuts = cut_pdf_into_periods(prob_density_function_model, 1/float(m_freq), step_size) for c in cuts: axes[0].plot(c, color="gray", alpha=0.2) axes[0].set_title("model") mean_model = np.mean(cuts, axis=0) axes[0].plot(mean_model, color="black") means_cell = [] for spikes_cell in spikes_dictionary[m_freq]: prob_density_cell = spiketimes_calculate_pdf(spikes_cell[0], step_size) cuts_cell = cut_pdf_into_periods(prob_density_cell, 1/float(m_freq), step_size) for c in cuts_cell: axes[1].plot(c, color="gray", alpha=0.15) print(cuts_cell.shape) means_cell.append(np.mean(cuts_cell, axis=0)) means_cell = np.array(means_cell) total_mean_cell = np.mean(means_cell, axis=0) axes[1].set_title("cell") axes[1].plot(total_mean_cell, color="black") axes[2].set_title("difference") diff = [(total_mean_cell[i]-mean_model[i]) for i in range(len(total_mean_cell))] axes[2].plot(diff) axes[3].plot(total_mean_cell) axes[3].plot(mean_model) plt.show() plt.close() def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005): trials_rate_list = [] step_size = model.get_parameters()["step_size"] for _ in range(trials): v1, spikes = model.simulate(stimulus, total_time_s=sim_length) binary = np.zeros(int(sim_length/step_size)) spikes = [int(s / step_size) for s in spikes] for s_idx in spikes: binary[s_idx] = 1 kernel = gaussian_kernel(kernel_width, step_size) rate = np.convolve(binary, kernel, mode='same') trials_rate_list.append(rate) times = [np.arange(0, sim_length, step_size) for _ in range(trials)] t, mean_rate = hF.calculate_mean_of_frequency_traces(times, trials_rate_list, step_size) return mean_rate def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005): length = int(spikes[len(spikes)-1] / step_size)+1 binary = np.zeros(length) spikes = [int(s / step_size) for s in spikes] for s_idx in spikes: binary[s_idx] = 1 kernel = gaussian_kernel(kernel_width, step_size) rate = np.convolve(binary, kernel, mode='same') return rate def cut_pdf_into_periods(pdf, period, step_size, factor=1.5): idx_period_length = int(period/float(step_size)) offset_per_step = period/float(step_size) - idx_period_length cut_length = int(period / float(step_size) * factor) cuts = [] num_of_cuts = int(len(pdf) / idx_period_length) if len(pdf) - (num_of_cuts * idx_period_length + (num_of_cuts * offset_per_step)) < cut_length - idx_period_length: num_of_cuts -= 1 if num_of_cuts <= 0: raise RuntimeError("Probability density function to short to cut.") for i in np.arange(0, num_of_cuts, 1): offset_correction = int(offset_per_step * i) start_idx = i*idx_period_length + offset_correction end_idx = (i*idx_period_length)+cut_length + offset_correction cuts.append(np.array(pdf[start_idx: end_idx])) cuts = np.array(cuts) if len(cuts.shape) < 2: print("Fishy....") return cuts def gaussian_kernel(sigma, dt): x = np.arange(-4. * sigma, 4. * sigma, dt) y = np.exp(-0.5 * (x / sigma) ** 2) / np.sqrt(2. * np.pi) / sigma return y if __name__ == '__main__': main()