from models.LIFACnoise import LifacNoiseModel from Baseline import BaselineModel from FiCurve import FICurveModel import numpy as np import matplotlib.pyplot as plt import copy import os SEARCH_WIDTH = 3 SEARCH_PRECISION = 40 CONTRASTS = np.arange(-0.4, 0.45, 0.1) def main(): test_effect_of_two_variables() quit() model_parameters1 = {'threshold': 1, 'step_size': 5e-05, 'a_zero': 2, 'delta_a': 0.2032269898801589, 'mem_tau': 0.011314027210564803, 'noise_strength': 0.056724809998220195, 'v_zero': 0, 'v_base': 0, 'tau_a': 0.05958195972016753, 'input_scaling': 119.81500448274554, 'dend_tau': 0.0027746086464721723, 'v_offset': -24.21875, 'refractory_period': 0.0006} model_parameters2 = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2, 'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623, 'mem_tau': 0.004828473985707999, 'noise_strength': 0.017132801387559883, 'v_zero': 0, 'dend_tau': 0.0015230454266819539, 'refractory_period': 0.0006} parameters_to_test = ["input_scaling", "refractory_period", "dend_tau", "mem_tau", "noise_strength", "v_offset", "delta_a", "tau_a"] # parameters_to_test = ["refractory_period", "input_scaling"] effect_data = [] for p in parameters_to_test: print("Working on parameter " + p) effect_data.append(test_parameter_effect(model_parameters2, p, 600)) plot_effects(effect_data, "./figures/variable_effect/") def test_effect_of_two_variables(): eod_freqs = np.arange(100, 1001, 50) ref_periods = np.arange(0, 0.00201, 0.0002) variables = ("bf", "vs", "sc", "cv", "burst", "f_inf_s", "f_zero_s") colorbar_labels = ("Frequency in Hz", "Vector strength", "serial correlation lag=1", "Coefficient of Variation", "Burstiness", "f_inf slope", "f_zero slope") # base eod frequency would be 771! base_parameters = {'step_size': 5e-05, 'mem_tau': 0.0076958612706114595, 'v_base': 0, 'v_zero': 0, 'threshold': 1, 'v_offset': -37.5, 'input_scaling': 181.40702315746051, 'delta_a': 0.333391796423963, 'tau_a': 0.17301586167067445, 'a_zero': 2, 'noise_strength': 0.017424670423939775, 'dend_tau': 0.0037179224836952356, 'refractory_period': 0.0010602702699897444} # base eod frequency would be 657! base_parameters = {'refractory_period': 0.0008347981797599925, 'v_base': 0, 'v_zero': 0, 'a_zero': 2, 'step_size': 5e-05, 'delta_a': 0.10570085698152036, 'threshold': 1, 'input_scaling': 85.7818875779873, 'mem_tau': 0.01094261953657057, 'tau_a': 0.07741757133763925, 'v_offset': -10.15625, 'noise_strength': 0.03080655781041302, 'dend_tau': 0.0013624430015225777} effects = [] for eod_freq in eod_freqs: effects_with_const_eod_freq = [] for ref_period in ref_periods: model_parameters = copy.deepcopy(base_parameters) model_parameters["refractory_period"] = ref_period effects_with_const_eod_freq.append(test_model(model_parameters, eod_freq)) effects.append(effects_with_const_eod_freq) if not os.path.exists("./figures/eod_and_ref_period_effect/"): os.makedirs("./figures/eod_and_ref_period_effect/") for x, variable in enumerate(variables): matrix = np.zeros((len(eod_freqs), len(ref_periods))) for i in range(len(eod_freqs)): for j in range(len(ref_periods)): matrix[i, j] = effects[i][j][variable] fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6)) im = axes.imshow(matrix) cbar = axes.figure.colorbar(im, ax=axes) cbar.ax.set_ylabel(colorbar_labels[x], rotation=-90, va="bottom") axes.set_title(variable) axes.set_xlabel("Refractory periods in ms") axes.set_ylabel("EOD frequency in Hz") axes.set_xticks(np.arange(len(ref_periods))) axes.set_yticks(np.arange(len(eod_freqs))) # ... and label them with the respective list entries axes.set_xticklabels(["{:.2f}".format(r*1000) for r in ref_periods]) axes.set_yticklabels(eod_freqs) plt.setp(axes.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") axes.set_ylabel("Eod frequencies") plt.tight_layout() plt.savefig("./figures/eod_and_ref_period_effect/" + variable + ".png") plt.close() def test_model(model_parameters, eod_freq): model = LifacNoiseModel(model_parameters) print(model.get_parameters()) fi_curve = FICurveModel(model, CONTRASTS, eod_freq, trials=10) f_inf_s = fi_curve.get_f_inf_slope() f_inf_v = fi_curve.get_f_inf_frequencies() f_zero_s = fi_curve.get_f_zero_fit_slope_at_stimulus_value(0.1) f_zero_v = fi_curve.get_f_zero_frequencies() baseline = BaselineModel(model, eod_freq, trials=3) bf = baseline.get_baseline_frequency() vs = baseline.get_vector_strength() sc = baseline.get_serial_correlation(1)[0] cv = baseline.get_coefficient_of_variation() burst = baseline.get_burstiness() return {"f_inf_s": f_inf_s, "f_inf_v": f_inf_v, "f_zero_s": f_zero_s, "f_zero_v": f_zero_v, "bf": bf, "vs": vs, "sc": sc, "cv": cv, "burst": burst} def test_parameter_effect(model_parameters, test_parameter, eod_freq): model_parameters = copy.deepcopy(model_parameters) start_value = model_parameters[test_parameter] start = start_value*(1/SEARCH_WIDTH) end = start_value*SEARCH_WIDTH step = (end - start) / SEARCH_PRECISION values = np.arange(start, end+step, step) bf = [] vs = [] sc = [] cv = [] burst = [] f_inf_s = [] f_inf_v = [] f_zero_s = [] f_zero_v = [] broken_i = [] for i in range(len(values)): model_parameters[test_parameter] = values[i] model = LifacNoiseModel(model_parameters) fi_curve = FICurveModel(model, CONTRASTS, eod_freq, trials=10) f_inf_s.append(fi_curve.get_f_inf_slope()) f_inf_v.append(fi_curve.get_f_inf_frequencies()) f_zero_s.append(fi_curve.get_f_zero_fit_slope_at_stimulus_value(0.1)) f_zero_v.append(fi_curve.get_f_zero_frequencies()) if not os.path.exists("./figures/f_point_detection/"): os.makedirs("./figures/f_point_detection/") detection_save_path = "./figures/f_point_detection/{}_{:.4f}/".format(test_parameter, values[i]) if not os.path.exists(detection_save_path): os.makedirs(detection_save_path) fi_curve.plot_f_point_detections(detection_save_path) baseline = BaselineModel(model, eod_freq, trials=3) bf.append(baseline.get_baseline_frequency()) vs.append(baseline.get_vector_strength()) sc.append(baseline.get_serial_correlation(2)) cv.append(baseline.get_coefficient_of_variation()) burst.append(baseline.get_burstiness()) values = list(values) if len(broken_i) > 0: broken_i = sorted(broken_i, reverse=True) for i in broken_i: del values[i] return ParameterEffectData(values, test_parameter, bf, vs, sc, cv, burst, f_inf_s, f_inf_v, f_zero_s, f_zero_v) # plot_effects(values, test_parameter, bf, vs, sc, cv, f_inf_s, f_inf_v, f_zero_s, f_zero_v) def plot_effects(par_effect_data_list, save_path=None): names = ("bf", "vs", "sc", "cv", "burstiness", "f_inf_s", "f_inf_v", "f_zero_s", "f_zero_v") fig, axes = plt.subplots(len(names), len(par_effect_data_list), figsize=(4*len(par_effect_data_list), 4*len(names)), sharex="col") for j in range(len(par_effect_data_list)): ped = par_effect_data_list[j] ranges = ((0, max(ped.get_data("bf")) * 1.1), (0, 1), (-1, 1), (0, 1), (0, 1), (0, max(ped.get_data("f_inf_s")) * 1.1), (0, 800), (0, max(ped.get_data("f_zero_s")) * 1.1), (0, 10000)) values = ped.values for i in range(len(names)): y_data = ped.get_data(names[i]) axes[i, j].plot(values, y_data) if names[i] == "f_zero_v": axes[i, j].set_yscale('log') axes[i, j].set_ylim(ranges[i]) else: axes[i, j].set_ylim(ranges[i]) if j == 0: axes[i, j].set_ylabel(names[i]) if i == 0: axes[i, j].set_title(ped.test_parameter) plt.tight_layout() if save_path is not None: plt.savefig(save_path + "variable_effect_master_plot.png") else: plt.show() plt.close() class ParameterEffectData: data_names = ("bf", "vs", "sc", "cv", "burstiness","f_inf_s", "f_inf_v" "f_zero_s", "f_zero_v") def __init__(self, values, test_parameter, bf, vs, sc, cv, burstiness, f_inf_s, f_inf_v, f_zero_s, f_zero_v): self.values = values self.test_parameter = test_parameter self.bf = bf self.vs = vs self.sc = sc self.cv = cv self.f_inf_s = f_inf_s self.f_inf_v = f_inf_v self.f_zero_s = f_zero_s self.f_zero_v = f_zero_v self.burstiness = burstiness def get_data(self, name): if name == "bf": return self.bf elif name == "vs": return self.vs elif name == "sc": return self.sc elif name == "cv": return self.cv elif name == "f_inf_s": return self.f_inf_s elif name == "f_inf_v": return self.f_inf_v elif name == "f_zero_s": return self.f_zero_s elif name == "f_zero_v": return self.f_zero_v elif name == "burstiness": return self.burstiness else: raise ValueError("Unknown attribute name!") if __name__ == '__main__': main()