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06e9bc2468
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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, input_file, 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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idx_a = input_file.find('/') |
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idx_b = input_file[idx_a+1::].find('/') |
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name_file = input_file[idx_a+1:idx_b+idx_a+1] |
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inputfile_path = 'results/' + name_file + '/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_values = { |
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3: 4800, |
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4: 7200, |
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5: 11520, |
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6: 11520, |
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2: 75 |
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} |
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true_values_C = { |
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3: 0.0004, |
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4: 0.0004, |
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5: 0.0003, |
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6: 0.0003, |
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} |
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meas_flag = False |
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RC_mod = True |
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line_split = 1.5 |
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current_val = [] |
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current_val_C = [] |
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ids_type = [] |
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labels = [] |
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ids = [] |
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for bnd_c in inputfile['estimation']['boundary_conditions']: |
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if 'windkessel' in bnd_c['type']: |
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for bnd_set in inputfile['boundary_conditions']: |
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if bnd_c['id'] == bnd_set['id']: |
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ids.append(bnd_c['id']) |
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ids_type.append('windkessel') |
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current_val.append(bnd_set['parameters']['R_d']) |
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labels.append('$R_' + str(bnd_c['id'])) |
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if RC_mod: |
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current_val_C.append(bnd_set['parameters']['C']) |
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labels.append('$C_' + str(bnd_c['id'])) |
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elif 'dirichlet' in bnd_c['type']: |
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current_val.append(inputfile['boundary_conditions'][0]['parameters']['U']) |
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ids.append(bnd_c['id']) |
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ids_type.append('dirichlet') |
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labels.append('$U') |
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dim = dat['theta'].shape[-1] |
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fig1, axes = plt.subplots(1,1,figsize=(8,6)) |
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axes.set_ylabel(r'$\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(labels) |
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if meas_flag: |
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t_und = t[0::30] |
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t_und = np.append( t_und , [t[-1]]) |
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meas_mark = t_und*0 |
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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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idx = 0 |
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idc = 0 |
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for i in range(len(ids)): |
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cur_key = ids[i] |
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true_level = np.log(true_values[ids[i]]/current_val[i])/np.log(2) |
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rec_value = np.round(2**theta[-1, idx]*current_val[i],2) |
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#curve = theta[:,i] + line_split*i |
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#dash_curve = line_split*i + t*0 + true_level |
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curve = theta[:,idx] + line_split*idx - true_level |
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dash_curve = line_split*idx + t*0 |
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axes.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key]) + '$') |
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axes.fill_between(t, curve - np.sqrt(P[:, idx, idx]), curve + np.sqrt(P[:, idx, idx]), alpha=0.3, color=col_) |
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legends_=next(legends) |
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axes.plot(t, dash_curve , color=col_,ls='--') |
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if RC_mod: |
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if i<len(current_val_C): |
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true_level_C = np.log(true_values_C[ids[i]]/current_val_C[i])/np.log(2) |
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rec_value_C = np.round(2**theta[-1, idc]*current_val_C[idc],6) |
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curve_C = theta[:,idx+1] + line_split*(idx+1) - true_level_C |
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dash_curve_C = line_split*(idx+1) + t*0 |
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#print(true_values_C[cur_key_C]) |
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axes.plot(t, curve_C , '-', color=col_,label= legends_ + '= ' + str(rec_value_C) + '/' + str(true_values_C[cur_key]) + '$') |
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axes.fill_between(t, curve_C - np.sqrt(P[:, idx+1, idx+1]), curve_C + np.sqrt(P[:, idx+1, idx+1]), alpha=0.3, color=col_) |
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axes.plot(t, dash_curve_C , color=col_,ls='--') |
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legends_=next(legends) |
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idx +=1 |
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idc +=1 |
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if meas_flag: |
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axes.plot(t_und, meas_mark + line_split*idx, marker = 'x', color='red') |
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col_ = next(col) |
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idx +=1 |
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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(true_values)) |
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#print('Recon values: \t {a}:{b} '.format(a=ids[:],b=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, args.file,deparameterize=args.deparameterize, ref=args.ref) |
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