update

kalman/graphics/figure2.py

@@ -52,42 +52,31 @@ def plot_parameters(dat, input_file, deparameterize=False, ref=None):
         inputfile = yaml.full_load(file)
 
 
-    #true_values = {
-    #        3: 3400,
-    #        4: 4200,
-    #        5: 11000,
-    #        6: 7800,
-    #        2: 100
-    #        }
   
     true_values = {
             3: 4800,
-            4: 7020,
+            4: 7200,
             5: 11520,
             6: 11520,
             2: 75
             }
 
+    true_values_C = {
+            3: 0.0004,
+            4: 0.0004,
+            5: 0.0003,
+            6: 0.0003,
+            }
 
 
-    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
-        }
 
 
+    meas_flag = False
+    RC_mod = True
+    line_split = 1.5
     current_val = []
+    current_val_C = []
+    ids_type = []
     labels = []
     ids = []
 
@@ -97,14 +86,23 @@ def plot_parameters(dat, input_file, deparameterize=False, ref=None):
             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('$R_' + str(bnd_c['id']))
+                    if RC_mod:
+                        current_val_C.append(bnd_set['parameters']['C'])
+                        labels.append('$C_' + str(bnd_c['id']))
+
 
         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')
 
+
+
+
     dim = dat['theta'].shape[-1]
     fig1, axes = plt.subplots(1,1,figsize=(8,6))
 
@@ -116,9 +114,12 @@ def plot_parameters(dat, input_file, deparameterize=False, ref=None):
 
     col = cycle(['C0', 'C1', 'C2', 'C3','C4'])
     ls = cycle(['-', '-', '--', '--', ':', ':', '-.', '-.'])
-    #legends = cycle(['$R_3$','$R_4$','$R_5$','$R_6$','$U$'])
     legends = cycle(labels)
-    
+
+    if meas_flag:
+        t_und = t[0::30]
+        t_und = np.append( t_und , [t[-1]])
+        meas_mark = t_und*0
 
     col_ = next(col)
     ls_ = next(ls)
@@ -127,23 +128,51 @@ def plot_parameters(dat, input_file, deparameterize=False, ref=None):
     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)
+    idx = 0
+    idc = 0
+
+    for i in range(len(ids)):
         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_)
-        
+        true_level =  np.log(true_values[ids[i]]/current_val[i])/np.log(2)
+        rec_value = np.round(2**theta[-1, idx]*current_val[i],2)
 
-        axes.plot(t,1.5*i + t*0 + true_level , color=col_,ls='--')
-        col_ = next(col)
+        #curve = theta[:,i] + line_split*i
+        #dash_curve = line_split*i + t*0 + true_level
+        
+        curve = theta[:,idx] + line_split*idx - true_level
+        dash_curve = line_split*idx + t*0
+        
+        axes.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$')
+        axes.fill_between(t, curve - np.sqrt(P[:, idx, idx]), curve + np.sqrt(P[:, idx, idx]), alpha=0.3, color=col_)
         legends_=next(legends)
+        axes.plot(t, dash_curve , color=col_,ls='--')
+
+
+        if RC_mod:
+        
+            if i<len(current_val_C):
+                true_level_C = np.log(true_values_C[ids[i]]/current_val_C[i])/np.log(2)
+                rec_value_C = np.round(2**theta[-1, idc]*current_val_C[idc],6)
+                curve_C = theta[:,idx+1] + line_split*(idx+1) - true_level_C
+                dash_curve_C = line_split*(idx+1) + t*0
+                #print(true_values_C[cur_key_C])
+                axes.plot(t, curve_C , '-', color=col_,label= legends_ + '= ' + str(rec_value_C) + '/' + str(true_values_C[cur_key])  + '$')
+                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_)
+                axes.plot(t, dash_curve_C , color=col_,ls='--')
+                legends_=next(legends)
+                idx +=1
+                idc +=1
+
+
+        if meas_flag:
+            axes.plot(t_und, meas_mark + line_split*idx, marker = 'x', color='red')
+
+        col_ = next(col)
+        idx +=1
 
     axes.legend(fontsize=14,loc='lower right')
     axes.set_xlim([-0.01,0.81])
@@ -152,7 +181,7 @@ def plot_parameters(dat, input_file, deparameterize=False, ref=None):
     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)))
+    #print('Recon values: \t {a}:{b} '.format(a=ids[:],b=np.round(2**theta[-1, :]*current_val,2)))
 
 
 

kalman/graphics/figure3.py

@@ -0,0 +1,244 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from itertools import cycle
+import argparse
+import pickle
+import yaml
+
+
+from matplotlib import rc
+rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
+rc('text', usetex=True)
+
+import matplotlib.font_manager
+
+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
+            }
+
+    true_values_C = {
+            3: 0.0004,
+            4: 0.0004,
+            5: 0.0003,
+            6: 0.0003,
+            }
+
+
+
+
+    meas_flag = False
+    RC_flag = False
+    line_split = 1.5
+    current_val = []
+    current_val_C = []
+    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('$R_' + str(bnd_c['id']))
+                    if RC_flag:
+                        current_val_C.append(bnd_set['parameters']['C'])
+                        labels.append('$C_' + str(bnd_c['id']))
+
+
+        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('$U')
+
+
+
+
+    dim = dat['theta'].shape[-1]
+    fig1, axes1 = plt.subplots(1,1,figsize=(12,6))
+    if RC_flag:
+        fig2, axes2 = plt.subplots(1,1,figsize=(12,6))
+    
+
+    t = dat['times']
+    theta = dat['theta']
+    P = dat['P_theta']
+
+    col = cycle(['C0', 'C1', 'C2', 'C3','C4'])
+    ls = cycle(['-', '-', '--', '--', ':', ':', '-.', '-.'])
+    legends = cycle(labels)
+
+    if meas_flag:
+        t_und = t[0::30]
+        t_und = np.append( t_und , [t[-1]])
+        meas_mark = t_und*0
+
+    col_ = next(col)
+    ls_ = next(ls)
+    legends_=next(legends)
+
+    if dim == 1:
+        theta = theta.reshape((-1, 1))
+        P = P.reshape((-1, 1, 1))
+    
+
+
+    idx = 0
+    idc = 0
+
+
+    for i in range(len(ids)):
+        cur_key = ids[i]
+
+        true_level =  np.log(true_values[ids[i]]/current_val[i])/np.log(2)
+        rec_value = np.round(2**theta[-1, idx]*current_val[i],2)
+
+        
+        #curve = theta[:,idx] + line_split*idx - true_level
+        #dash_curve = line_split*idx + t*0
+
+        curve = 2**theta[:, idx]*current_val[i]
+        std_down =  2**(-np.sqrt(P[:, idx, idx]))*curve
+        std_up =  2**np.sqrt(P[:, idx, idx])*curve
+        dash_curve = true_values[ids[i]]  + t*0
+        
+
+        if ids_type[i] == 'dirichlet':
+            pass
+            #axes3.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$')
+            #axes3.fill_between(t, curve - np.sqrt(P[:, idx, idx]), curve + np.sqrt(P[:, idx, idx]), alpha=0.3, color=col_)
+            #legends_=next(legends)
+            #axes3.plot(t, dash_curve , color=col_,ls='--')
+        else:
+            axes1.plot(t, curve , '-', color=col_,label= legends_ + '= ' + str(rec_value) + '/' + str(true_values[cur_key])  + '$', linewidth = 2)
+            axes1.fill_between(t, std_down, std_up, alpha=0.3, color=col_)
+            axes1.plot(t, dash_curve , color=col_,ls='--')
+            legends_=next(legends)
+
+
+        if RC_flag:
+            if i<len(current_val_C):
+                true_level_C = np.log(true_values_C[ids[i]]/current_val_C[i])/np.log(2)
+                rec_value_C = np.round(2**theta[-1, idc]*current_val_C[idc],6)
+
+                curve_C = 2**theta[:, idx+1]*current_val_C[idc]
+                dash_curve_C = true_values_C[ids[i]] + t*0
+                std_C_down =  2**(-np.sqrt(P[:, idx+1, idx+1]))*curve_C
+                std_C_up =  2**np.sqrt(P[:, idx+1, idx+1])*curve_C
+                
+                axes2.plot(t, curve_C , '-', color=col_,label= legends_ + '= ' + str(rec_value_C) + '/' + str(true_values_C[cur_key])  + '$', linewidth = 2)
+                axes2.fill_between(t, std_C_down, std_C_up, alpha=0.3, color=col_)
+                axes2.plot(t, dash_curve_C , color=col_,ls='--')
+                legends_=next(legends)
+                idx +=1
+                idc +=1
+
+
+        if meas_flag:
+            axes1.plot(t_und, meas_mark + line_split*idx, marker = 'x', color='red')
+
+        col_ = next(col)
+        idx +=1
+
+
+    axes1.set_ylabel(r'$R_d$',fontsize=22)
+    axes1.legend(fontsize=18,loc='upper right')
+    axes1.set_xlim([-0.01,0.81])
+    axes1.set_xlabel(r'$t  (s)$',fontsize=22)
+    plt.savefig('C.png')
+
+    if RC_flag:
+
+        axes2.set_ylabel(r'$C$',fontsize=22)
+        axes2.legend(fontsize=18,loc='upper right')
+        axes2.set_xlim([-0.01,0.81])
+        axes2.set_xlabel(r'$t  (s)$',fontsize=22)
+        fig2.savefig('C.png')
+
+    fig1.savefig('Rd.png')
+    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)

kalman/input_files/aorta.yaml

@@ -1,6 +1,6 @@
 
 mesh: './meshes/coaortaH1.h5'
-# Physical parameters of the fluid
+# Physical parameters of the fluid  
 fluid:
     density: 1.2
     dynamic_viscosity: 0.035
@@ -8,7 +8,7 @@ fluid:
     implicit_windkessel: True
 
 io:
-    write_path: 'results/aorta25_zPb_3R'
+    write_path: 'results/Rz_Pa_vnoise'
     restart:
         path: ''  # './projects/nse_coa3d/results/test_restart2/' 
         time: 0
@@ -26,9 +26,10 @@ boundary_conditions:
         #                 -U*sin(DOLFIN_PI*(t-1.6)/Th)*(t<= 1.6+Th )*(t>1.6) + (t<2.4)*(1.6+Th<t)*(U*DOLFIN_PI/Th*(t-1.6-Th)*exp(-(t-1.6-Th)*beta))' ]
         value: ['0','0','-U*sin(DOLFIN_PI*t/Th)*(t<=Th) + (Th<t)*(U*DOLFIN_PI/Th*(t-Th)*exp(-(t-Th)*beta))']
         parameters:
-            #U: 75
-            #U: 150 #Pa
-            U: 40 #Pb
+            #U: 75  #P0
+            U: 150 #Pa
+            #U: 100 #Pg
+            #U: 40 #Pc
             Th: 0.36
             beta: 70
             t: 0
@@ -41,10 +42,8 @@ boundary_conditions:
         type: 'windkessel'
         parameters:
             R_p: 200
-            C: 0.0005
-            #R_d: 4800
-            #R_d: 9000 #Pa
-            R_d: 2000 #Pb
+            C: 0.0004
+            R_d: 4800
             p0: 85
             conv: 1333.223874   
     - 
@@ -52,10 +51,16 @@ boundary_conditions:
         type: 'windkessel'
         parameters:
             R_p: 480
-            C: 0.00045
-            #R_d: 7200
-            #R_d: 12000 #Pa
-            R_d: 4000 #Pb
+            #C: 0.0004  # P0
+            C: 0.0005   # Pa
+            #C: 0.0010   # Pb
+            #C: 0.0001   # Pc
+            #C: 0.0008   # Pg
+            #R_d: 7200  #P0
+            R_d:  8760  #Pa
+            #R_d: 17520 #Pb x2
+            #R_d: 10000  #Pg 
+            #R_d: 4000  #Pc
             p0: 85
             conv: 1333.223874   
     - 
@@ -63,10 +68,17 @@ boundary_conditions:
         type: 'windkessel'
         parameters:
             R_p: 520
-            C: 0.00045
-            #R_d: 11520
-            #R_d: 23000 #Pa
-            R_d: 6000 #Pb
+            #C: 0.0003 # REFERENCE
+            C: 0.0005 # Pa
+            #C: 0.0010 # Pb
+            #C: 0.0001 # Pc
+            #C: 0.0008 # Pg
+            #R_d: 11520 # REFERENCE
+            R_d:  8760  #Pa
+            #R_d: 17520 #Pb x2
+            #R_d: 26280 #Pc x3
+            #R_d: 10000 #Pg
+            #R_d: 4000 #Pc
             p0: 85
             conv: 1333.223874   
     - 
@@ -74,10 +86,17 @@ boundary_conditions:
         type: 'windkessel'
         parameters:
             R_p: 520
-            C: 0.00045
-            R_d: 11520
-            #R_d: 23000 #Pa
-            #R_d: 6000 #Pb
+            #C: 0.0003 # REFERENCE
+            C: 0.0005 #Pa
+            #C: 0.0010 #Pb
+            #C: 0.0001 #Pc
+            #C: 0.0008 #Pg
+            #R_d: 11520 # REFERENCE
+            R_d:  8760 #Pa
+            #R_d: 17520 #Pb x2
+            #R_d: 26280 #Pc x3
+            #R_d: 10000 #Pg
+            #R_d: 4000 #Pc
             p0: 85
             conv: 1333.223874   
 
@@ -143,22 +162,25 @@ linear_solver:
 
 estimation:
     boundary_conditions:
-        - 
-            id: 3
-            type: 'windkessel'
-            initial_stddev: 1
+        #- 
+        #    id: 3
+        #    type: 'windkessel'
+        #    initial_stddev: 1
         - 
             id: 4
             type: 'windkessel'
+            mode: 'Rd'
             initial_stddev: 1
         - 
             id: 5
             type: 'windkessel'
+            mode: 'Rd'
+            initial_stddev: 1
+        - 
+            id: 6
+            type: 'windkessel'
+            mode: 'Rd'
             initial_stddev: 1
-        #- 
-        #    id: 6
-        #    type: 'windkessel'
-        #    initial_stddev: 1
         - 
             id: 2
             type: 'dirichlet'
@@ -171,13 +193,13 @@ estimation:
             mesh: '/home/yeye/NuMRI/kalman/meshes/coaortaH3_leo2.0.h5'
             #mesh: './meshes/coaortaH1.h5'
             fe_degree: 1
-            xdmf_file: 'measurements/aorta25_z/Perturbation/Mg15V120/u_all.xdmf'
-            file_root: 'measurements/aorta25_z/Perturbation/Mg15V120/u{i}.h5'
-            #xdmf_file: 'measurements/aorta25_z/u_all.xdmf'
-            #file_root: 'measurements/aorta25_z/u{i}.h5'
+            #xdmf_file: 'measurements/aorta_zdir/Perturbation/Mg15V120/u_all.xdmf'
+            #file_root: 'measurements/aorta_zdir/Perturbation/Mg15V120/u{i}.h5'
+            xdmf_file: 'measurements/aorta_zdir_vnoise/u_all.xdmf'
+            file_root: 'measurements/aorta_zdir_vnoise/u{i}.h5'
             indices: 0 # indices of checkpoints to be processed. 0 == all
             velocity_direction: [0,0,1]
-            noise_stddev: 55     # standard deviation of Gaussian noise
+            noise_stddev: 45     # standard deviation of Gaussian noise
     
     roukf:
         particles: 'simplex' # unique or simplex
@@ -185,4 +207,4 @@ estimation:
         reparameterize: True
         ODV_functional:
             enable: False
-            VENC: 102    #  102,120%   59,70%   42 50%,   21,25%
+            VENC: 244