from CellData import CellData from models.LIFACnoise import LifacNoiseModel from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus import helperFunctions as hF import numpy as np from warnings import warn import matplotlib.pyplot as plt class Baseline: def __init__(self): self.baseline_frequency = -1 self.serial_correlation = [] self.vector_strength = -1 self.coefficient_of_variation = -1 def get_baseline_frequency(self): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") def get_serial_correlation(self, max_lag): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") def get_vector_strength(self): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") def get_coefficient_of_variation(self): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") def get_interspike_intervals(self): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") def plot_baseline(self, save_path=None, time_length=0.2): raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS") @staticmethod def plot_baseline_given_data(time, eod, v1, spiketimes, sampling_interval, save_path=None, time_length=0.2): """ plots the stimulus / eod, together with the v1, spiketimes and frequency :return: """ length_data_points = int(time_length / sampling_interval) start_idx = int(len(time) * 0.5 - length_data_points * 0.5) start_idx = start_idx if start_idx >= 0 else 0 end_idx = int(len(time) * 0.5 + length_data_points * 0.5) + 1 end_idx = end_idx if end_idx <= len(time) else len(time) spiketimes = np.array(spiketimes) spiketimes_part = spiketimes[(spiketimes >= time[start_idx]) & (spiketimes < time[end_idx])] fig, axes = plt.subplots(3, 1, sharex="col", figsize=(12, 8)) fig.suptitle("Baseline middle part ({:.2f} seconds)".format(time_length)) axes[0].plot(time[start_idx:end_idx], eod[start_idx:end_idx]) axes[0].set_ylabel("Stimulus [mV]") max_v1 = max(v1[start_idx:end_idx]) axes[1].plot(time[start_idx:end_idx], v1[start_idx:end_idx]) axes[1].plot(spiketimes_part, [max_v1 for _ in range(len(spiketimes_part))], 'o', color='orange') axes[1].set_ylabel("V1-Trace [mV]") t, f = hF.calculate_time_and_frequency_trace(spiketimes_part, sampling_interval) axes[2].plot(t, f) axes[2].set_ylabel("ISI-Frequency [Hz]") axes[2].set_xlabel("Time [s]") if save_path is not None: plt.savefig(save_path + "baseline.png") else: plt.show() plt.close() def plot_interspike_interval_histogram(self, save_path=None): isi = np.array(self.get_interspike_intervals()) * 1000 # change unit to milliseconds maximum = max(isi) bins = np.arange(0, maximum * 1.01, 0.1) plt.title('Baseline ISIs') plt.xlabel('ISI in ms') plt.ylabel('Count') plt.hist(isi, bins=bins) if save_path is not None: plt.savefig(save_path + "isi-histogram.png") else: plt.show() plt.close() def plot_serial_correlation(self, max_lag, save_path=None): plt.title("Baseline Serial correlation") plt.xlabel("Lag") plt.ylabel("Correlation") plt.ylim((-1, 1)) plt.plot(np.arange(1, max_lag+1, 1), self.get_serial_correlation(max_lag)) if save_path is not None: plt.savefig(save_path + "serial_correlation.png") else: plt.show() plt.close() class BaselineCellData(Baseline): def __init__(self, cell_data: CellData): super().__init__() self.data = cell_data def get_baseline_frequency(self): if self.baseline_frequency == -1: base_freqs = [] for freq in self.data.get_mean_isi_frequencies(): delay = self.data.get_delay() sampling_interval = self.data.get_sampling_interval() if delay < 0.1: warn("BaselineCellData:get_baseline_Frequency(): Quite short delay at the start.") idx_start = int(0.025 / sampling_interval) idx_end = int((delay - 0.025) / sampling_interval) base_freqs.append(np.mean(freq[idx_start:idx_end])) self.baseline_frequency = np.median(base_freqs) return self.baseline_frequency def get_vector_strength(self): if self.vector_strength == -1: times = self.data.get_base_traces(self.data.TIME) eods = self.data.get_base_traces(self.data.EOD) v1_traces = self.data.get_base_traces(self.data.V1) self.vector_strength = hF.calculate_vector_strength_from_v1_trace(times, eods, v1_traces) return self.vector_strength def get_serial_correlation(self, max_lag): if len(self.serial_correlation) != max_lag: serial_cors = [] for spiketimes in self.data.get_base_spikes(): sc = hF.calculate_serial_correlation(spiketimes, max_lag) serial_cors.append(sc) serial_cors = np.array(serial_cors) mean_sc = np.mean(serial_cors, axis=0) self.serial_correlation = mean_sc return self.serial_correlation def get_coefficient_of_variation(self): if self.coefficient_of_variation == -1: spiketimes = self.data.get_base_spikes() # CV (stddev of ISI divided by mean ISI (np.diff(spiketimes)) cvs = [] for st in spiketimes: st = np.array(st) cvs.append(hF.calculate_coefficient_of_variation(st)) self.coefficient_of_variation = np.mean(cvs) return self.coefficient_of_variation def get_interspike_intervals(self): spiketimes = self.data.get_base_spikes() # CV (stddev of ISI divided by mean ISI (np.diff(spiketimes)) isis = [] for st in spiketimes: st = np.array(st) isis.extend(np.diff(st)) return isis def plot_baseline(self, save_path=None, time_length=0.2): # eod, v1, spiketimes, frequency time = self.data.get_base_traces(self.data.TIME)[0] eod = self.data.get_base_traces(self.data.EOD)[0] v1_trace = self.data.get_base_traces(self.data.V1)[0] spiketimes = self.data.get_base_spikes()[0] self.plot_baseline_given_data(time, eod, v1_trace, spiketimes, self.data.get_sampling_interval(), save_path, time_length) class BaselineModel(Baseline): simulation_time = 30 def __init__(self, model: LifacNoiseModel, eod_frequency): super().__init__() self.model = model self.eod_frequency = eod_frequency self.stimulus = SinusoidalStepStimulus(eod_frequency, 0) self.v1, self.spiketimes = model.simulate_fast(self.stimulus, self.simulation_time) self.eod = self.stimulus.as_array(0, self.simulation_time, model.get_sampling_interval()) self.time = np.arange(0, self.simulation_time, model.get_sampling_interval()) def get_baseline_frequency(self): if self.baseline_frequency == -1: self.baseline_frequency = hF.calculate_mean_isi_freq(self.spiketimes) return self.baseline_frequency def get_vector_strength(self): if self.vector_strength == -1: self.vector_strength = hF.calculate_vector_strength_from_spiketimes(self.time, self.eod, self.spiketimes, self.model.get_sampling_interval()) return self.vector_strength def get_serial_correlation(self, max_lag): if len(self.serial_correlation) != max_lag: self.serial_correlation = hF.calculate_serial_correlation(self.spiketimes, max_lag) return self.serial_correlation def get_coefficient_of_variation(self): if self.coefficient_of_variation == -1: self.coefficient_of_variation = hF.calculate_coefficient_of_variation(self.spiketimes) return self.coefficient_of_variation def get_interspike_intervals(self): return np.diff(self.spiketimes) def plot_baseline(self, save_path=None, time_length=0.2): self.plot_baseline_given_data(self.time, self.eod, self.v1, self.spiketimes, self.model.get_sampling_interval(), save_path, time_length) def get_baseline_class(data, eod_freq=None) -> Baseline: if isinstance(data, CellData): return BaselineCellData(data) if isinstance(data, LifacNoiseModel): if eod_freq is None: raise ValueError("The EOD frequency is needed for the BaselineModel Class.") return BaselineModel(data, eod_freq) raise ValueError("Unknown type: Cannot find corresponding Baseline class. data was type:" + str(type(data)))