NuMRI/kalman/graphics/figureRd.py

import matplotlib.pyplot as plt
import numpy as np
from itertools import cycle
import argparse
import pickle
import yaml


#import matplotlib.font_manager
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)



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: 4800,
            4: 7200,
            5: 11520,
            6: 11520,
            2: 75
            }


    dim = dat['theta'].shape[-1]

    current_val = []
    ids_type = []
    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'])
                    ids_type.append('windkessel')
                    current_val.append(bnd_set['parameters']['R_d'])
                    labels.append('$R_' + str(bnd_c['id']-3))


        elif 'dirichlet' in bnd_c['type']:
            current_val.append(inputfile['boundary_conditions'][0]['parameters']['U'])
            ids.append(bnd_c['id'])
            ids_type.append('dirichlet')
            labels.append('$U')


    if 'windkessel' in ids_type:
        fig1, axes1 = plt.subplots(1,1,figsize=(12,7))
    if 'dirichlet' in ids_type: 
        fig3, axes3 = plt.subplots(1,1,figsize=(12,7))





    t = dat['times']
    theta = dat['theta']
    P = dat['P_theta']

    #col = cycle(['C0', 'C1', 'C2', 'C3','C4'])
    col = cycle(['orangered', 'dodgerblue', 'limegreen', 'C3','C4'])
    
    legends = cycle(labels)

    col_ = next(col)
    legends_=next(legends)

    if dim == 1:
        theta = theta.reshape((-1, 1))
        P = P.reshape((-1, 1, 1))
    
    idx = 0

    for i in range(len(ids)):

        cur_key = ids[i]
        rec_value = np.round(2**theta[-1, idx]*current_val[i],2)
        curve = 2**theta[:, idx]*current_val[i]
        std_down =  2**(-np.sqrt(P[:, idx, idx]))*curve
        std_up =  2**np.sqrt(P[:, idx, idx])*curve
        dash_curve = true_values[ids[i]]  + t*0


        if ids_type[i] == 'dirichlet':
            axes3.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$', linewidth = 5)
            axes3.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
            legends_=next(legends)
            axes3.plot(t, dash_curve , color=col_,ls='--' , linewidth = 3)
            axes3.set_ylabel(r'$U$',fontsize=36)
            axes3.legend(fontsize=36,loc='upper right')
            axes3.set_xlim([0,0.45])
            axes3.set_ylim([8,180])
            axes3.set_xlabel(r'$t  (s)$',fontsize=36)
            axes3.set_box_aspect(1/2)
            plt.xticks(fontsize=28)
            plt.yticks(fontsize=28)
            #plt.savefig('U_' + name_file + '.png')
            plt.close(fig3)
        else:
            axes1.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$', linewidth = 4)
            axes1.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
            axes1.plot(t, dash_curve , color=col_,ls='--',linewidth = 3)
            legends_=next(legends)


        col_ = next(col)
        idx +=1




    axes1.set_ylabel(r'$R_d$',fontsize=30)
    axes1.legend(fontsize=36,loc='upper right')
    axes1.set_xlim([0,0.51])
    axes1.set_ylim([-1000,65000])
    axes1.set_box_aspect(1/2)
    plt.xticks(fontsize=28)
    plt.yticks(fontsize=28)
    axes1.set_xlabel(r'$t  (s)$',fontsize=36)

    path_paper = '/home/yeye/A_aliasing_kalman/latex/0_preprint/Figures/'
    path_paper = '/home/yeye/Desktop/'
    #fig1.savefig('Rd_'+ name_file +'.png')
    fig1.savefig(path_paper + 'Rd_'+ name_file +'.png')
    




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)