import argparse import os import numpy as np import matplotlib.pyplot as plt from scipy.stats import pearsonr from ModelFit import get_best_fit def main(): # parser = argparse.ArgumentParser() # parser.add_argument("dir", help="folder containing the cell folders with the fit results") # args = parser.parse_args() dir_path = "results/invivo_results/" # args.dir # if not os.path.isdir(dir_path): # print("Argument dir is not a directory.") # parser.print_usage() # exit(0) fits_info = get_fit_info(dir_path) # errors = calculate_percent_errors(fits_info) # create_boxplots(errors) # labels, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=False) # create_correlation_plot(labels, corr_values, corrected_p_values) # labels, corr_values, corrected_p_values = parameter_correlations(fits_info) # create_correlation_plot(labels, corr_values, corrected_p_values) create_parameter_distributions(get_parameter_values(fits_info)) pass def calculate_percent_errors(fits_info): errors = {} for cell in sorted(fits_info.keys()): for behaviour in fits_info[cell][1].keys(): if behaviour not in errors.keys(): errors[behaviour] = [] if fits_info[cell][2][behaviour] == 0: if fits_info[cell][1][behaviour] == 0: errors[behaviour].append(0) else: print("Cannot calc % error if reference is 0") continue errors[behaviour].append((fits_info[cell][1][behaviour] - fits_info[cell][2][behaviour]) / fits_info[cell][2][behaviour]) return errors def get_parameter_values(fits_info): par_keys = sorted(["input_scaling", "delta_a", "mem_tau", "noise_strength", "refractory_period", "tau_a", "v_offset", "dend_tau"]) parameter_values = {} for cell in sorted(fits_info.keys()): for par in par_keys: if par not in parameter_values.keys(): parameter_values[par] = [] parameter_values[par].append(fits_info[cell][0][par]) return parameter_values def get_behaviour_values(fits_info): behaviour_values_cell = {} behaviour_values_model = {} for cell in sorted(fits_info.keys()): for behaviour in fits_info[cell][1].keys(): if behaviour not in behaviour_values_cell.keys(): behaviour_values_cell[behaviour] = [] behaviour_values_model[behaviour] = [] behaviour_values_model[behaviour].append(fits_info[cell][1][behaviour]) behaviour_values_cell[behaviour].append(fits_info[cell][2][behaviour]) return behaviour_values_cell, behaviour_values_model def behaviour_correlations(fits_info, model_values=True): bv_cell, bv_model = get_behaviour_values(fits_info) if model_values: behaviour_values = bv_model else: behaviour_values = bv_cell labels = sorted(behaviour_values.keys()) corr_values = np.zeros((len(labels), len(labels))) p_values = np.ones((len(labels), len(labels))) for i in range(len(labels)): for j in range(len(labels)): c, p = pearsonr(behaviour_values[labels[i]], behaviour_values[labels[j]]) corr_values[i, j] = c p_values[i, j] = p corrected_p_values = p_values * sum(range(len(labels))) return labels, corr_values, corrected_p_values def parameter_correlations(fits_info): parameter_values = get_parameter_values(fits_info) labels = sorted(parameter_values.keys()) corr_values = np.zeros((len(labels), len(labels))) p_values = np.ones((len(labels), len(labels))) for i in range(len(labels)): for j in range(len(labels)): c, p = pearsonr(parameter_values[labels[i]], parameter_values[labels[j]]) corr_values[i, j] = c p_values[i, j] = p corrected_p_values = p_values * sum(range(len(labels))) return labels, corr_values, corrected_p_values def get_fit_info(folder): fits_info = {} for item in os.listdir(folder): cell_folder = os.path.join(folder, item) results = get_best_fit(cell_folder) cell_behaviour, model_behaviour = results.get_behaviour_values() fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour] return fits_info def create_correlation_plot(labels, correlations, p_values): cleaned_cors = np.zeros(correlations.shape) for i in range(correlations.shape[0]): for j in range(correlations.shape[1]): if abs(p_values[i, j]) < 0.05: cleaned_cors[i, j] = correlations[i, j] fig, ax = plt.subplots() im = ax.imshow(cleaned_cors, vmin=-1, vmax=1) cbar = ax.figure.colorbar(im, ax=ax) cbar.ax.set_ylabel("Correlation coefficient", rotation=-90, va="bottom") # We want to show all ticks... ax.set_xticks(np.arange(len(labels))) ax.set_yticks(np.arange(len(labels))) # ... and label them with the respective list entries ax.set_xticklabels(labels) ax.set_yticklabels(labels) # Rotate the tick labels and set their alignment. plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Loop over data dimensions and create text annotations. for i in range(len(labels)): for j in range(len(labels)): text = ax.text(j, i, "{:.2f}".format(correlations[i, j]), ha="center", va="center", color="w") fig.tight_layout() plt.show() def create_boxplots(errors): labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())] y_values = [errors[k] for k in sorted(errors.keys())] plt.boxplot(y_values) plt.xticks(np.arange(1, len(y_values)+1, 1), labels, rotation=45) plt.tight_layout() plt.show() plt.close() pass def create_parameter_distributions(par_values): fig, axes = plt.subplots(4, 2) if len(par_values.keys()) != 8: print("not eight parameters") labels = sorted(par_values.keys()) axes_flat = axes.flatten() for i, l in enumerate(labels): min_v = min(par_values[l]) * 0.95 max_v = max(par_values[l]) * 1.05 step = (max_v - min_v) / 15 bins = np.arange(min_v, max_v+step, step) axes_flat[i].hist(par_values[l], bins=bins) axes_flat[i].set_title(l) plt.tight_layout() plt.show() plt.close() def create_behaviour_distributions(cell_b_values, model_b_values): pass if __name__ == '__main__': main()