import matplotlib.pyplot as plt import numpy as np from itertools import cycle import argparse import pickle import yaml def is_ipython(): ''' Check if script is run in IPython. Returns: bool: True if IPython, else False ''' try: get_ipython() ipy = True except NameError: ipy = False return ipy def load_data(file): ''' Load numpy data from file. Returns dict: data dictionary ''' dat = np.load(file) return dat def plot_parameters(dat, deparameterize=False, ref=None): ''' Plot the parameters in separate subplots with uncertainties. Args: dat (dict): data dictionary deparameterize (bool): flag indicating if parameters should be deparameterized via 2**theta ref: reference value to be plotted with parameters ''' if is_ipython(): plt.ion() inputfile_path = 'results/aorta_C/input.yaml' with open(inputfile_path) as file: inputfile = yaml.full_load(file) #true_val = [10,250,250,250,30] true_val = [94,250,683,615,30] current_val = [] current_val.append(inputfile['boundary_conditions'][2]['value'][0]) current_val.append(inputfile['boundary_conditions'][3]['value'][0]) current_val.append(inputfile['boundary_conditions'][4]['value'][0]) current_val.append(inputfile['boundary_conditions'][5]['value'][0]) current_val.append(inputfile['boundary_conditions'][1]['parameters']['U']) dim = dat['theta'].shape[-1] fig1, axes = plt.subplots(1,1,figsize=(8,6)) axes.set_ylabel(r'$\theta$',fontsize=18) t = dat['times'] theta = dat['theta'] P = dat['P_theta'] col = cycle(['C0', 'C1', 'C2', 'C3','C4']) ls = cycle(['-', '-', '--', '--', ':', ':', '-.', '-.']) legends = cycle(['$R_3$','$R_4$','$R_5$','$R_6$','$U$']) col_ = next(col) ls_ = next(ls) legends_=next(legends) if dim == 1: theta = theta.reshape((-1, 1)) P = P.reshape((-1, 1, 1)) for i in range(dim): axes.plot(t, theta[:, i] + 1.5*i, '-', color=col_,label=legends_) axes.fill_between(t, theta[:, i] + 1.5*i - np.sqrt(P[:, i, i]), theta[:, i] + 1.5*i + np.sqrt(P[:, i, i]), alpha=0.3, color=col_) true_level = np.log(true_val[i]/current_val[i])/np.log(2) axes.plot(t,1.5*i + t*0 + true_level , color=col_,ls='--') col_ = next(col) legends_=next(legends) axes.legend(fontsize=14,loc='lower right') axes.set_xlim([-0.01,0.81]) axes.set_xlabel(r'time (s)',fontsize=18) # print('theta_peak: \t {}'.format(theta[round(len(theta)/2), :])) print('Final value theta: \t {}'.format(theta[-1, :])) print('Deparameterized: 2^theta_end: \t {}'.format(2**theta[-1, :])) print('Real values: \t {}'.format(np.round(2**theta[-1, :]*current_val,2))) plt.savefig('windk_res') if not is_ipython(): plt.show() def get_parser(): parser = argparse.ArgumentParser( description=''' Plot the time evolution of the ROUKF estimated parameters. To execute in IPython:: %run plot_roukf_parameters.py [-d] [-r N [N \ ...]] file ''', formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('file', type=str, help='path to ROUKF stats file') parser.add_argument('-d', '--deparameterize', action='store_true', help='deparameterize the parameters by 2**theta') parser.add_argument('-r', '--ref', metavar='N', nargs='+', default=None, type=float, help='Reference values for parameters') return parser if __name__ == '__main__': args = get_parser().parse_args() dat = load_data(args.file) plot_parameters(dat, deparameterize=args.deparameterize, ref=args.ref)