asd
This commit is contained in:
parent
639d1cdd74
commit
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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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#import matplotlib.font_manager
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from matplotlib import rc
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rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
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rc('text', usetex=True)
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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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dim = dat['theta'].shape[-1]
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current_val = []
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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('$R_' + str(bnd_c['id']-3))
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elif 'dirichlet' in bnd_c['type']:
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current_val.append(inputfile['boundary_conditions'][0]['parameters']['U'])
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ids.append(bnd_c['id'])
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ids_type.append('dirichlet')
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labels.append('$U')
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if 'windkessel' in ids_type:
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fig1, axes1 = plt.subplots(1,1,figsize=(12,7))
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if 'dirichlet' in ids_type:
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fig3, axes3 = plt.subplots(1,1,figsize=(12,7))
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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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col = cycle(['orangered', 'dodgerblue', 'limegreen', 'C3','C4'])
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legends = cycle(labels)
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col_ = next(col)
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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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for i in range(len(ids)):
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cur_key = ids[i]
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rec_value = np.round(2**theta[-1, idx]*current_val[i],2)
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curve = 2**theta[:, idx]*current_val[i]
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std_down = 2**(-np.sqrt(P[:, idx, idx]))*curve
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std_up = 2**np.sqrt(P[:, idx, idx])*curve
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dash_curve = true_values[ids[i]] + t*0
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if ids_type[i] == 'dirichlet':
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axes3.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key]) + '$', linewidth = 5)
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axes3.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
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legends_=next(legends)
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axes3.plot(t, dash_curve , color=col_,ls='--' , linewidth = 3)
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axes3.set_ylabel(r'$U$',fontsize=36)
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axes3.legend(fontsize=36,loc='upper right')
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axes3.set_xlim([0,0.45])
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axes3.set_ylim([8,180])
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axes3.set_xlabel(r'$t (s)$',fontsize=36)
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axes3.set_box_aspect(1/2)
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plt.xticks(fontsize=28)
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plt.yticks(fontsize=28)
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#plt.savefig('U_' + name_file + '.png')
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plt.close(fig3)
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else:
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axes1.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key]) + '$', linewidth = 4)
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axes1.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
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axes1.plot(t, dash_curve , color=col_,ls='--',linewidth = 3)
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legends_=next(legends)
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col_ = next(col)
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idx +=1
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axes1.set_ylabel(r'$R_d$',fontsize=30)
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axes1.legend(fontsize=36,loc='upper right')
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axes1.set_xlim([0,0.51])
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axes1.set_ylim([-1000,65000])
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axes1.set_box_aspect(1/2)
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plt.xticks(fontsize=28)
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plt.yticks(fontsize=28)
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axes1.set_xlabel(r'$t (s)$',fontsize=36)
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path_paper = '/home/yeye/A_aliasing_kalman/latex/0_preprint/Figures/'
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path_paper = '/home/yeye/Desktop/'
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#fig1.savefig('Rd_'+ name_file +'.png')
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fig1.savefig(path_paper + 'Rd_'+ name_file +'.png')
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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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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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#import matplotlib.font_manager
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from matplotlib import rc
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rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
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rc('text', usetex=True)
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def plot_parameters():
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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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name_file = ['SNR12V120_Pf','SNR12V70_Pf','SNR12V30_Pf']
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#name_file = ['SNR12V120_Pf_MAG','SNR12V70_Pf_MAG','SNR12V30_Pf_MAG']
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#name_file = ['SNR12V120_Pb_MAG','SNR12V70_Pb_MAG','SNR12V30_Pb_MAG']
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name_file = ['slice2.3_Pa']
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vencs = ['180','105','45']
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path0 = '/home/yeye/Desktop/kalman/results/'
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fig, axes = plt.subplots(1,1,figsize=(12,7))
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col = cycle(['orangered', 'dodgerblue', 'limegreen', 'C3','C4'])
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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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for nn,name in enumerate(name_file):
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path1 = path0 + name + '/'
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inputfile_path = path1 + 'input.yaml'
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dat = np.load(path1 + 'theta_stats.npz')
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with open(inputfile_path) as file:
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inputfile = yaml.full_load(file)
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col_ = next(col)
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dim = dat['theta'].shape[-1]
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current_val = []
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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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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('$U')
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t = dat['times']
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theta = dat['theta']
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P = dat['P_theta']
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legends = cycle(labels)
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legends_=next(legends)
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if dim == 1:
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theta = theta.reshape((-1, 1))
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P = P.reshape((-1, 1, 1))
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idx = 0
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for i in range(len(ids)):
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cur_key = ids[i]
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rec_value = np.round(2**theta[-1, idx]*current_val[i],2)
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curve = 2**theta[:, idx]*current_val[i]
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std_down = 2**(-np.sqrt(P[:, idx, idx]))*curve
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std_up = 2**np.sqrt(P[:, idx, idx])*curve
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dash_curve = true_values[ids[i]] + t*0
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if ids_type[i] == 'dirichlet':
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axes.plot(t, curve , '-', color=col_,label= '$(venc \ '+ vencs[nn] + ' \ cm/s) \ U = ' + str(rec_value) + '/' + str(true_values[cur_key]) + '$', linewidth = 5)
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#axes.plot(t, curve , '-', color=col_,label= legends_ + vencs[nn] + ' \ cm/s$', linewidth = 5)
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axes.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
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legends_=next(legends)
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axes.plot(t, dash_curve , color='black',ls='--' , linewidth = 3)
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idx +=1
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axes.set_ylabel(r'$U$',fontsize=36)
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axes.legend(fontsize=30,loc='upper right')
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axes.set_xlim([0,0.35])
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axes.set_ylim([10,160])
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axes.set_xlabel(r'$t (s)$',fontsize=36)
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axes.set_box_aspect(1/2)
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plt.xticks(fontsize=28)
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plt.yticks(fontsize=28)
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plt.savefig('U.png')
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plt.show()
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#path_paper = '/home/yeye/A_aliasing_kalman/latex/0_preprint/Figures/'
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#fig1.savefig('Rd_'+ name_file +'.png')
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if __name__ == '__main__':
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plot_parameters()
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@ -8,7 +8,7 @@ fluid:
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state_velocity: 'update'
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io:
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write_path: 'results/Pb_Hz2.3_Hz5.7'
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write_path: 'results/Pb_Hz2.3_SNR12V30'
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restart:
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path: '' # './projects/nse_coa3d/results/test_restart2/'
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time: 0
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@ -146,7 +146,7 @@ fem:
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streamline_diffusion:
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enabled: False
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parameter: 'standard' # standard, shakib, codina, klr
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length_scale: 'metric' # average, max, metric
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length_scale: 'average' # average, max, metric
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parameter_element_constant: True
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Cinv: ~
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monolithic:
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@ -192,36 +192,39 @@ estimation:
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measurements:
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#-
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# #mesh: '/home/yeye/NuMRI/kalman/meshes/coaortaH3_leo2.0.h5'
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# mesh: '/home/yeye/Desktop/slices/slice_Hz5.7.h5'
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# fe_degree: 0
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# xdmf_file: 'measurements/slice_Hz5.7/Perturbation/Mg12V30/u_all.xdmf'
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# file_root: 'measurements/slice_Hz5.7/Perturbation/Mg12V30/u{i}.h5'
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# #xdmf_file: 'measurements/slice_Hz5.7/u_all.xdmf'
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# #file_root: 'measurements/slice_Hz5.7/u{i}.h5'
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# indices: 0 # indices of checkpoints to be processed. 0 == all
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# velocity_direction: [0,0,1]
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# #noise_stddev: 0 # standard deviation of Gaussian noise
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# #noise_stddev: 22.39 # SNR 12 slice 5.7
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# #noise_stddev: 15.76 # SNR 15 slice 5.7
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# #noise_stddev: 8.75 # SNR 15 slice 2.3
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# #noise_stddev: 12.15 # SNR 12 slice 2.3
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# noise_stddev: 0.269
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# VENC: 47
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# module_meas_file_root: 'measurements/slice_Hz5.7/Perturbation/Mg12V30/module/M{i}.h5'
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-
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#mesh: '/home/yeye/NuMRI/kalman/meshes/coaortaH3_leo2.0.h5'
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mesh: '/home/yeye/Desktop/slices/slice_Hz2.3.h5'
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fe_degree: 0
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xdmf_file: 'measurements/slice_Hz2.3/Perturbation/Mg12V120/u_all.xdmf'
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file_root: 'measurements/slice_Hz2.3/Perturbation/Mg12V120/u{i}.h5'
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#xdmf_file: 'measurements/slice_Hz5.7/u_all.xdmf'
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#file_root: 'measurements/slice_Hz5.7/u{i}.h5'
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xdmf_file: 'measurements/slice_Hz2.3/Perturbation/Mg12V30/u_all.xdmf'
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file_root: 'measurements/slice_Hz2.3/Perturbation/Mg12V30/u{i}.h5'
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indices: 0 # indices of checkpoints to be processed. 0 == all
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velocity_direction: [0,0,1]
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#noise_stddev: 0 # standard deviation of Gaussian noise
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#noise_stddev: 22.39 # SNR 12 slice 5.7
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#noise_stddev: 15.76 # SNR 15 slice 5.7
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#noise_stddev: 8.75 # SNR 15 slice 2.3
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noise_stddev: 12.15 # SNR 12 slice 2.3
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-
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mesh: '/home/yeye/Desktop/slices/slice_Hz5.7.h5'
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fe_degree: 0
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xdmf_file: 'measurements/slice_Hz5.7/Perturbation/Mg12V120/u_all.xdmf'
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file_root: 'measurements/slice_Hz5.7/Perturbation/Mg12V120/u{i}.h5'
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indices: 0 # indices of checkpoints to be processed. 0 == all
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velocity_direction: [0,0,1]
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noise_stddev: 22.39 # SNR 12 slice 5.7
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noise_stddev: 0.25
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VENC: 26
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module_meas_file_root: 'measurements/slice_Hz2.3/Perturbation/Mg12V30/module/M{i}.h5'
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roukf:
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particles: 'simplex' # unique or simplex
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observation_operator: 'postprocessing' #state or postprocessing
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reparameterize: True
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MAG_functional:
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enable: False
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VENC: 61
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module_meas_file_root: 'measurements/aorta_zdir/Perturbation/Mg15V30/module/M{i}.h5'
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MAG_functional: True
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