import matplotlib.pyplot as plt import numpy as np from itertools import cycle import argparse import pickle 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): ''' 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() fig1, axes = plt.subplots(1, 1,figsize=(8,6)) axes.set_ylabel(r'$\theta$',fontsize=18) col = cycle(['C0', 'C1', 'C2', 'C3']) for k in dat.keys(): t = dat[k]['times'] theta = dat[k]['theta'] P = dat[k]['P_theta'] theta = theta.reshape((-1, 1)) P = P.reshape((-1, 1, 1)) #theta = 2**theta*float(k) sP = np.sqrt(P[:,0,0]) sP_up = 2**sP sP_down = 2**(-sP) col_ = next(col) axes.plot(t, theta[:, 0], '-', c=col_) #axes.fill_between(t, theta[:, 0]*sP_down[:], theta[:, 0]*sP_up[:], alpha=0.3,color=col_) axes.fill_between(t, theta[:, 0] - np.sqrt(P[:, 0, 0]),theta[:, 0] + np.sqrt(P[:, 0, 0]), alpha=0.3,color=col_) axes.set_xlabel(r'time',fontsize=18) axes.plot(t, t*0 + np.log(30/float(k))/np.log(2), '-', c='black',ls='--') axes.set_xlim([-0.01,0.2]) 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() files = ['10','30','60'] dat_array = {} for ff in files: path = args.file + ff + '/theta_stats.npz' dat_array[ff] = load_data(path) plot_parameters(dat_array)