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