import os from models.LIFACnoise import LifacNoiseModel from Baseline import get_baseline_class from FiCurve import get_fi_curve_class from CellData import CellData import numpy as np import functions as fu import matplotlib.pyplot as plt def get_best_fit(folder_path): min_err = np.inf min_item = "" for item in os.listdir(folder_path): item_path = os.path.join(folder_path, item) err = ModelFit(item_path).comparable_error() if err < min_err: min_err = err min_item = item return ModelFit(os.path.join(folder_path, min_item)) class ModelFit: def __init__(self, folder_path): self.path = folder_path self.parameter_file_name = "parameters_info.txt" self.value_file = "value_comparision.tsv" self.fi_comp_img = "fi_curve_comparision.png" self.isi_hist_img = "isi-histogram.png" self.isi_hist_comp_img = "isi-histogram_comparision.png" self.model_f_inf_file = "model_fi_inf_values.npy" self.cell_f_inf_file = "cell_fi_inf_values.npy" self.model_f_zero_file = "model_fi_zero_values.npy" self.cell_f_zero_file = "cell_fi_zero_values.npy" def get_final_parameters(self): par_file_path = os.path.join(self.path, self.parameter_file_name) with open(par_file_path, 'r') as par_file: for line in par_file: line = line.strip().split('\t') if line[0] == "final_parameters:": return eval(line[1]) print("Final parameters not found! - ", self.path) return {} def get_start_parameters(self): par_file_path = os.path.join(self.path, self.parameter_file_name) with open(par_file_path, 'r') as par_file: for line in par_file: line = line.strip().split('\t') if line[0] == "start_parameters:": return dict(line[1]) print("Start parameters not found! - ", self.path) return {} def get_behaviour_values(self): values_file_path = os.path.join(self.path, self.value_file) cell_values = {} model_values = {} with open(values_file_path, 'r') as val_file: line = val_file.readline() # ignore headers for line in val_file: line = line.strip().split('\t') cell_values[line[0]] = float(line[1]) model_values[line[0]] = float(line[2]) return cell_values, model_values def get_fi_curve_comparision_image(self): path = os.path.join(self.path, self.fi_comp_img) if os.path.exists(path): return path else: raise FileNotFoundError("Fi-curve comparision image is missing. - " + self.path) def get_isi_histogram_image(self): path = os.path.join(self.path, self.isi_hist_img) if os.path.exists(path): return path else: raise FileNotFoundError("Isi-histogram image is missing. - " + self.path) def get_error_value(self): return self.path.split("_")[-1] def get_model(self): return LifacNoiseModel(self.get_final_parameters()) def get_cell_path(self): with open(os.path.join(self.path, "cell_data_path.txt"), "r") as f: cell_path = f.readline().strip() return cell_path def get_cell_data(self): return CellData(self.get_cell_path()) def get_model_f_inf_values(self): path = os.path.join(self.path, self.model_f_inf_file) return np.load(path) def get_model_f_zero_values(self): path = os.path.join(self.path, self.model_f_zero_file) return np.load(path) def get_cell_f_inf_values(self): path = os.path.join(self.path, self.cell_f_inf_file) return np.load(path) def get_cell_f_zero_values(self): path = os.path.join(self.path, self.cell_f_zero_file) return np.load(path) def comparable_error(self): cell_values, model_values = self.get_behaviour_values() error = 0 bf = "baseline_frequency" error += abs(cell_values[bf] - model_values[bf]) / 5 vs = "vector_strength" error += abs(cell_values[vs] - model_values[vs]) / 0.1 sc = "serial_correlation" error += abs(cell_values[sc] - model_values[sc]) / 0.1 burst = "Burstiness" error += abs(cell_values[burst] - model_values[burst]) / 0.05 cv = "coefficient_of_variation" error += abs(cell_values[cv] - model_values[cv]) / 0.1 f_inf_slope = "f_inf_slope" error += abs(cell_values[f_inf_slope] - model_values[f_inf_slope]) / 5 # f_zero_sloe = "f_zero_slope" # error += abs(cell_values[f_zero_sloe] - model_values[f_zero_sloe]) / 100 c_f_inf_values = self.get_cell_f_inf_values() c_f_zero_values = self.get_cell_f_zero_values() m_f_inf_values = self.get_model_f_inf_values() m_f_zero_values = self.get_cell_f_zero_values() error_f_inf = 0 for m_value, c_value in zip(m_f_inf_values, c_f_inf_values): error_f_inf += abs(c_value - m_value) / 10 error_f_inf = error_f_inf / len(m_f_inf_values) error += error_f_inf error_f_zero = 0 for m_value, c_value in zip(m_f_zero_values, c_f_zero_values): error_f_zero += abs(c_value - m_value) / 10 error_f_zero = error_f_zero / len(m_f_zero_values) error += error_f_zero return error def generate_master_plot(self, save_path=None): model = self.get_model() cell = self.get_cell_data() fig, axes = plt.subplots(3, 1, figsize=(8, 10)) # isi histogram: axes[0].set_title("ISI-Histogram") axes[0].set_xlim((0, 50)) bins = np.arange(0, 50, 0.1) for data, name in zip((model, cell), ("model", "cell")): base = get_baseline_class(data, cell.get_eod_frequency(), trials=5) isis = np.array(base.get_interspike_intervals()) * 1000 axes[0].hist(isis, bins=bins, label=name, alpha=0.5, density=True) axes[0].legend() # fi_curve fi_curve_cell = get_fi_curve_class(cell, cell.get_fi_contrasts(), eod_freq=cell.get_eod_frequency(), trials=15) fi_curve_model = get_fi_curve_class(model, cell.get_fi_contrasts(), eod_freq=cell.get_eod_frequency(), trials=15) axes[1].set_title("Fi-Curve") min_x = min(min(fi_curve_cell.stimulus_values), min(fi_curve_model.stimulus_values)) max_x = max(max(fi_curve_cell.stimulus_values), max(fi_curve_model.stimulus_values)) step = (max_x - min_x) / 5000 x_values = np.arange(min_x, max_x + step, step) # plot baseline f_base_color = ("blue", "deepskyblue") f_inf_color = ("green", "limegreen") f_zero_color = ("red", "orange") median_baseline = np.median(fi_curve_cell.get_f_baseline_frequencies()) axes[1].plot((min_x, max_x), (median_baseline, median_baseline), color=f_base_color[0], label="cell med base") axes[1].plot(fi_curve_model.stimulus_values, fi_curve_model.get_f_baseline_frequencies(), 'o', color=f_base_color[1], label='model base') y_values = [fu.clipped_line(x, fi_curve_cell.f_inf_fit[0], fi_curve_cell.f_inf_fit[1]) for x in x_values] axes[1].plot(x_values, y_values, color=f_inf_color[0], label='f_inf_fit cell') axes[1].plot(fi_curve_model.stimulus_values, fi_curve_model.get_f_inf_frequencies(), 'o', color=f_inf_color[1], label='f_inf model') popt = fi_curve_cell.f_zero_fit axes[1].plot(x_values, [fu.full_boltzmann(x, popt[0], popt[1], popt[2], popt[3]) for x in x_values], color=f_zero_color[0], label='f_0_fit cell') axes[1].plot(fi_curve_model.stimulus_values, fi_curve_model.get_f_zero_frequencies(), 'o', color=f_zero_color[1], label='f_zero model') axes[1].set_title("cell model comparision") axes[1].set_xlabel("Stimulus value - contrast") axes[1].legend() # Value table: cell_values, model_values = self.get_behaviour_values() collabel = sorted(cell_values.keys()) clust_data = [[], []] for k in collabel: clust_data[0].append(cell_values[k]) clust_data[1].append(model_values[k]) axes[2].axis('tight') axes[2].axis('off') table = axes[2].table(cellText=clust_data, colLabels=collabel, rowLabels=("cell", "model"), loc='center') fig.suptitle(cell.get_cell_name() + "_comp_err: {:.2f}".format(self.comparable_error())) if save_path is None: plt.show() else: plt.savefig(save_path + cell.get_cell_name() + "_master_plot.pdf")