NuMRI/kalman/graphics/figure2.py

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, input_file, 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()

    idx_a = input_file.find('/')
    idx_b = input_file[idx_a+1::].find('/')
    name_file = input_file[idx_a+1:idx_b+idx_a+1]
    inputfile_path = 'results/' + name_file + '/input.yaml'


    with open(inputfile_path) as file:
        inputfile = yaml.full_load(file)


    #true_values = {
    #        3: 3400,
    #        4: 4200,
    #        5: 11000,
    #        6: 7800,
    #        2: 100
    #        }
  
    true_values = {
            3: 4800,
            4: 7020,
            5: 11520,
            6: 11520,
            2: 75
            }



    true_values_c = {
        3: 0.0008,
        4: 0.00034,
        5: 0.00034,
        6: 0.00034,
        2: 100
        }

    true_values_rp = {
        3: 10,
        4: 60,
        5: 220,
        6: 160,
        2: 100
        }


    current_val = []
    labels = []
    ids = []

    for bnd_c in inputfile['estimation']['boundary_conditions']:
        
        if 'windkessel' in bnd_c['type']:
            for bnd_set in inputfile['boundary_conditions']:
                if bnd_c['id'] == bnd_set['id']:
                    ids.append(bnd_c['id'])
                    current_val.append(bnd_set['parameters']['R_d'])
                    labels.append('$R_' + str(bnd_c['id']))

        elif 'dirichlet' in bnd_c['type']:
            current_val.append(inputfile['boundary_conditions'][0]['parameters']['U'])
            ids.append(bnd_c['id'])
            labels.append('$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(['$R_3$','$R_4$','$R_5$','$R_6$','$U$'])
    legends = cycle(labels)
    

    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):
        
        true_level =  np.log(true_values[ids[i]]/current_val[i])/np.log(2)
        rec_value = np.round(2**theta[-1, i]*current_val[i],2)
        cur_key = ids[i]

        axes.plot(t, theta[:, i] + 1.5*i, '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$')
        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_)
        

        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(true_values))
    print('Recon values: \t {a}:{b} '.format(a=ids[:],b=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, args.file,deparameterize=args.deparameterize, ref=args.ref)