# this script is designed to take processed experimental data resulting from tdms_processing, csv_processing # (kinematics in JCS with model offsets already removed, kinetics as forces/moments applied to the femur defined in the tibia coorindate system), # and the model that results from running the customization script, and, create a model that applies to experimental conditions to the # model by first converting the kinetics data into the image coorindate system, and then applying those kinetics as external forces to the # femur. kinematics load curves are also added, so that the kinematics can be easily applied to the cylindircal joint by setting # the translation/rotation of the joint to follow the load curve. # see main function at end of file for tips on running for a single model vs many models. import pandas as pd import numpy as np from lxml import etree as et import math import csv import os def myElement(tag, parent=None, text=None, attrib={}, **extra): """Shortcut to create an xml element (root or child), add text and other attributes""" e = et.Element(tag, attrib, **extra) if text: e.text = text if parent is not None: parent.append(e) return e def read_csv(csv_file): with open(csv_file, newline='', encoding='utf8') as my_file: file_reader = csv.reader(my_file, delimiter=',') file_contents = [] for row in file_reader: file_contents.append(row) contents_dict = {} column_names = file_contents.pop(0) for i, name in enumerate(column_names): if len(name) == 0: # empty continue values = [] for row in file_contents: v = row[i] if len(v) == 0: # empty values.append(np.nan) else: values.append(float(v)) contents_dict[name] = np.asarray(values) return contents_dict def convert_to_image(modelproperties_xml, kinetics_csv): """ convert kineitcs data from the experiment tibia cs to the image cs, save in a new csv file""" # get the tibia axes from the model properties file ModelProperties_tree = et.parse(modelproperties_xml) ModelProperties = ModelProperties_tree.getroot() landmarks = ModelProperties.find('Landmarks') tibia_x = landmarks.find('Xt_axis').text.split(',') tibia_y = landmarks.find('Yt_axis').text.split(',') tibia_z = landmarks.find('Zt_axis').text.split(',') tibia_x = [float(i) for i in tibia_x] tibia_y = [float(i) for i in tibia_y] tibia_z = [float(i) for i in tibia_z] tibia_axes = np.array([tibia_x,tibia_y,tibia_z]) tibia_in_image = tibia_axes.T # rotation matrix from tibia to image CS try: df = pd.read_csv(kinetics_csv, encoding='utf7') except: df = pd.read_csv(kinetics_csv, encoding='utf8') # note - this will only work if the data processing was used to process the data. # should make this less "hard coded" at a later time to avoid problems arising # from making a change in the data processing scripts loading_times = None moment_units = None # check to make sure where the last data point is. crop the data up the last data point fx = df['External Femur_x Load [N]'].values intial_len = len(fx) count = 0 while math.isnan(fx[-1]): fx = fx[:-1] count += 1 if count > 0: trunc_after = intial_len - count - 1 df = df.truncate(after = trunc_after) for col in df.columns: if 'Moment' in col: moment_units = col.split(' ')[-1] if 'Applied' in col or "Flexion Angle" in col: applied_load = df[col].values # sometimes an empty row appears at the end of the csv - probably dont need this after truncation above but just in case if math.isnan(applied_load[-1]): applied_load = applied_load[:-1] # this will give time spacing from 0 to 1, based on load loading_times = (applied_load) / (applied_load[-1]) # this is done explicity to ensure the data is in the correct order # but can probably ensure the order some other way to keep is as fx,fy,fz,mx,my,mz fx = df['External Femur_x Load [N]'].values fy = df['External Femur_y Load [N]'].values fz = df['External Femur_z Load [N]'].values mx = df['External Femur_x Moment '+ moment_units].values # units are differet in oks and knee hub, this takes care of that my = df['External Femur_y Moment '+ moment_units].values mz = df['External Femur_z Moment '+ moment_units].values Forces = np.array([fx, fy, fz]) Moments = np.array([mx, my, mz]) # apply that transformation to the forces, and moments (kinetics data) to get them in the image coordinate system Forces_image = np.matmul(tibia_in_image, Forces) Moments_image = np.matmul(tibia_in_image, Moments) # save data in new csv df2 = pd.DataFrame() df2['External Femur_x Load [N]'] = Forces_image[0] df2['External Femur_y Load [N]'] = Forces_image[1] df2['External Femur_z Load [N]'] = Forces_image[2] df2['External Femur_x Moment '+ moment_units] = Moments_image[0] df2['External Femur_y Moment '+ moment_units] = Moments_image[1] df2['External Femur_z Moment '+ moment_units] = Moments_image[2] df2.to_csv(kinetics_csv.replace('_TibiaCS', '_ImageCS')) return df2, loading_times def add_kinetics_load_curves(febio_spec_root, image_kinetics, loading_times, relative= False): # get this list from the image kinetics df, not hard coded loads = image_kinetics.columns # # loads = ['External Femur_x Load [N]','External Femur_y Load [N]','External Femur_z Load [N]', # 'External Femur_x Moment [Nm]','External Femur_y Moment [Nm]','External Femur_z Moment [Nm]'] LoadData = febio_spec_root.find('LoadData') # find the number of the last load curve lc_count = LoadData[-1].attrib["id"] lc_count = int(lc_count) lc_nums = [] # create a load curve for each load for load in loads: lc_count += 1 lc_nums.append(lc_count) load_values = image_kinetics[load].values if '[Nm]' in load: # if moments given in Nm need to convert to Nmm loadcurve = myElement('loadcurve', LoadData, name=load.replace('Nm', 'Nmm'), id=str(lc_count)) load_values = 1000*load_values else: loadcurve = myElement('loadcurve', LoadData, name=load, id=str(lc_count)) myElement('point', loadcurve, text = '0,0') myElement('point', loadcurve, text='1,0') if relative: # loading starting at time 1, relative loading so subtract initial condition time = 1.0 load_values = load_values - load_values[0] else: time = 2.0 # loading starting at time 2 - time 1 to 2 is to reach initial load value for a,v in enumerate(load_values): if math.isnan(v): pass else: t = loading_times[a] + time myElement('point', loadcurve, text='{0:.2f},{1:.3f}'.format(t,v)) return lc_nums def add_must_points(febio_spec_root, image_kinetics, loading_times, relative = False): """ create a load curve for dtmax, and change the plot_level parameter in the control section to plot_must_points""" LoadData = febio_spec_root.find('LoadData') # find the last load curve id last_id = LoadData[-1].attrib["id"] id = int(last_id) +1 # add the new load curve loadcurve = myElement("loadcurve", LoadData, name="must_points", id = str(id)) # from time 0 to 1 is prestrain myElement('point', loadcurve, text='0,0') myElement('point', loadcurve, text='1,1') if relative: # from 1 to 2 is applying experiment kinematics, take time steps the same as the experiment for i in loading_times + 1.0: if math.isnan(i): pass else: myElement('point', loadcurve, text='{0:.2f},0.1'.format(i)) else: # from 1 to 2 is initial loading, take steps of 0.1 --- cutting this out as sometimes causes convergence issues if change is small. # for i in np.arange(1.1, 2.0, 0.1): # myElement('point', loadcurve, text = '{0:.2f},0.1'.format(i)) # # from 2 to 3 is applying experiment kinematics, take time steps the same as the experiment for i in loading_times + 2.0: if math.isnan(i): pass else: myElement('point', loadcurve, text='{0:.2f},1'.format(i)) # for i in np.arange(2.0+time_step, 3.0+time_step, time_step): # myElement('point', loadcurve, text='{0:.2f},{1:.2f}'.format(i, time_step)) Step = febio_spec_root.find("Step") Control = Step.find("Control") # update the plot level parameter plot_level = Control.find("plot_level") plot_level.text = 'PLOT_MUST_POINTS' # update dtmax to include the load curve time_stepper = Control.find("time_stepper") dtmax = time_stepper.find("dtmax") dtmax.attrib["lc"] = str(id) def add_kinematics_load_curves(febio_spec_root, kinematics_csv , loading_times, relative = False): try: df = pd.read_csv(kinematics_csv, encoding='utf7') except: df = pd.read_csv(kinematics_csv, encoding='utf8') # extract kinematics channel names kinematics_names = [None,None,None,None,None,None] for col in df.columns: i=None cl = col.lower() if 'lateral' in cl or 'medial' in cl: i=0 elif 'anterior' in cl or 'posterior' in cl: i=1 elif 'superior' in cl or 'inferior' in cl: i=2 elif 'extension' in cl or 'flexion' in cl: i=3 elif 'adduction' in cl or 'abduction' in cl: i=4 elif 'internal' in cl or 'external' in cl: i=5 if i is not None: kinematics_names[i] = col LoadData = febio_spec_root.find('LoadData') # find the number of the last load curve lc_count = LoadData[-1].attrib["id"] lc_count = int(lc_count) lc_nums = [] for n in kinematics_names: lc_count += 1 lc_nums.append(lc_count) # kinematics_values = df[n].values kinematics_values = list(map(float, df[n].values)) # create a load curve if 'deg' in n.lower(): print_n = n.replace('deg','rad') else: print_n = n loadcurve = myElement('loadcurve', LoadData, name=print_n, id=str(lc_count)) myElement('point', loadcurve, text='0,0') myElement('point', loadcurve, text='1,0') if relative: time = 1.0 kinematics_values = np.array(kinematics_values) - kinematics_values[0] else: time = 2.0 for a, v in enumerate(kinematics_values): if math.isnan(v): pass else: t = loading_times[a] + time if 'deg' in n.lower(): myElement('point', loadcurve, text='{0:.2f},{1:.3f}'.format(t, np.radians(v))) # convert to radians else: myElement('point', loadcurve, text='{0:.2f},{1:.3f}'.format(t, v)) # convert to radians return lc_nums def change_existing_lc(kinematics_csv, febio_spec_root, loading_times, relative = False): try: df = pd.read_csv(kinematics_csv, encoding='utf7') except: df = pd.read_csv(kinematics_csv, encoding='utf8') # extension_rotation =list(map(float, df['Knee JCS Extension Rotation [deg]'].values)) LoadData = febio_spec_root.find('LoadData') for loadcurve in LoadData: if "prestrain" in loadcurve.attrib["name"]: if not relative: last_point = loadcurve[-1].text # find the prestrain value from the previous time point prstrn = last_point.split(',')[-1] myElement('point',loadcurve,text = '3,'+prstrn) elif "Loading_Curve" in loadcurve.attrib["name"]: if not relative: myElement('point', loadcurve, text= '3,1') else: pass # elif loadcurve.attrib['id'] == "9": # the flexion load curve # loadcurve.attrib["name"] = "flexion_angle" # loadcurve.remove(loadcurve[-1]) # if relative: # time = 1.0 # extension_rotation = np.array(extension_rotation) - extension_rotation[0] # else: # time = 2.0 # for a, v in enumerate(extension_rotation): # if math.isnan(v): # pass # else: # t = loading_times[a] + time # myElement('point', loadcurve, # text='{0:.2f},{1:.3f}'.format(t, np.radians(v))) # convert to radians def constraint_load_curves(Step, lc_nums): # make sure the rotation is set to 1 in the flexion constraint contraints = Step.find('Constraints') for const in contraints: try: # get the ids of the translation and rotation in the lc_nums list const_name = const.attrib["name"] if "Flexion" in const_name: ids = (0,3) elif "Abduction" in const_name: ids = (1,4) elif "Internal" in const_name: ids = (2,5) else: continue # set the load curves translation = const.find('translation') rotation = const.find('rotation') translation.text = "1.0" rotation.text = "1.0" translation.attrib["lc"] = str(lc_nums[ids[0]]) rotation.attrib["lc"] = str(lc_nums[ids[1]]) # prescribed_translation = const.find('prescribed_translation') # prescribed_rotation = const.find('prescribed_rotation') except KeyError: # if there is a constraint without a name pass def add_rigid_forces(boundary_secion, lc_nums): rigid_body = myElement('rigid_body',boundary_secion, mat="1") dof = ['x','y','z','Rx','Ry','Rz'] for i in range(6): myElement('force', rigid_body, bc = dof[i], lc = str(lc_nums[i]), text = '1.0') def write_file(xml_tree, new_filename): # Write the New File xml_tree.write(new_filename, xml_declaration=True, pretty_print=True) # make sure that it's pretty printing properly, by parsing and re-writing parser = et.XMLParser(remove_blank_text=True) new_feb_tree = et.parse(new_filename, parser) new_feb_tree.write(new_filename, xml_declaration=True, pretty_print=True) def make_model(modelproperties_xml, febio_file, kinetics_csv, kinematics_csv, name=None, relative = False): """ make a model to mimic experiment""" image_kinetics, loading_times = convert_to_image(modelproperties_xml, kinetics_csv) Febio_tree = et.parse(febio_file) febio_spec_root = Febio_tree.getroot() # add the load curves for the kinetics kinetics_lc_nums = add_kinetics_load_curves(febio_spec_root, image_kinetics, loading_times, relative=relative) # add time to in situ load curves change_existing_lc(kinematics_csv, febio_spec_root, loading_times, relative=relative) # add the kinematics load curves kineamtics_lc_nums = add_kinematics_load_curves(febio_spec_root, kinematics_csv, loading_times, relative=relative) # add the must points so febio gives output at the desired time steps add_must_points(febio_spec_root, image_kinetics, loading_times, relative=relative) # change the control settings for improved convergence Step = febio_spec_root.find('Step') Control = Step.find('Control') time_steps = Control.find('time_steps') step_size = Control.find('step_size') max_refs = Control.find('max_refs') min_residual = Control.find('min_residual') # try to take step size of 1 when possible (except for must points) if relative: time_steps.text = "2" if not relative: time_steps.text = "3" step_size.text = "1" # increase max refs, decrease min residual max_refs.text = "50" min_residual.text = "0.01" # add the loads to the femur Boundary = Step.find('Boundary') add_rigid_forces(Boundary, kinetics_lc_nums) # set the kinematics load curves in the constraints. set only the flexion rotation to on, others can be changed # manually later if the user wants. constraint_load_curves(Step, kineamtics_lc_nums) # re-write the file. If no name give, change the original febio file if name is None: new_name = febio_file else: new_name = os.path.join(os.path.dirname(febio_file), name+'.feb') write_file(Febio_tree, new_name) def oks003_calibration(): # modelprops_file = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/InSituStrain/ModelProperties.xml' modelprops_file = 'C:/oks/app/KneeHub/test/registration02/ModelProperties.xml' # AnteriorLaxity # febio_file = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/InSituStrain/Spring_Ties3/AnteriorLaxity/FeBio_custom.feb' # kinetics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_AP1_kinetics_in_TibiaCS.csv' # kinematics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_AP1_kinematics_in_JCS.csv' febio_file = 'C:/oks/app/KneeHub/test/AnteriorLaxity/FeBio_custom.feb' kinetics_csv = 'C:/oks/app/KneeHub/test/AnteriorLaxity/Laxity_0deg_AP1_kinetics_in_TibiaCS.csv' kinematics_csv = 'C:/oks/app/KneeHub/test/AnteriorLaxity/Laxity_0deg_AP1_kinematics_in_JCS.csv' # febio_file = 'C:/oks/app/KneeHub/test/tdms/Processed_Data/FeBio_custom.feb' # kinetics_csv = 'C:/oks/app/KneeHub/test/tdms/Processed_Data/Laxity_0deg_AP1_kinetics_in_TibiaCS.csv' # kinematics_csv = 'C:/oks/app/KneeHub/test/tdms/Processed_Data/Laxity_0deg_AP1_kinematics_in_JCS.csv' make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv) # PosteriorLaxity # febio_file = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/InSituStrain/Spring_Ties3/PosteriorLaxity/FeBio_custom.feb' # kinetics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_AP2_kinetics_in_TibiaCS.csv' # kinematics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_AP2_kinematics_in_JCS.csv' febio_file = 'C:/oks/app/KneeHub/test/PosteriorLaxity/FeBio_custom.feb' kinetics_csv = 'C:/oks/app/KneeHub/test/PosteriorLaxity/Laxity_0deg_AP2_kinetics_in_TibiaCS.csv' kinematics_csv = 'C:/oks/app/KneeHub/test/PosteriorLaxity/Laxity_0deg_AP2_kinematics_in_JCS.csv' make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv) # VarusLaxity # febio_file = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/InSituStrain/Spring_Ties3/VarusLaxity/FeBio_custom.feb' # kinetics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_VV1_kinetics_in_TibiaCS.csv' # kinematics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_VV1_kinematics_in_JCS.csv' febio_file = 'C:/oks/app/KneeHub/test/VarusLaxity/FeBio_custom.feb' kinetics_csv = 'C:/oks/app/KneeHub/test/VarusLaxity/Laxity_0deg_VV1_kinetics_in_TibiaCS.csv' kinematics_csv = 'C:/oks/app/KneeHub/test/VarusLaxity/Laxity_0deg_VV1_kinematics_in_JCS.csv' make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv) # ValgusLaxity # febio_file = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/InSituStrain/Spring_Ties3/ValgusLaxity/FeBio_custom.feb' # kinetics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_VV2_kinetics_in_TibiaCS.csv' # kinematics_csv = '/home/schwara2/Documents/Open_Knees/knee_hub/oks003/calibration/DataProcessing/Processed_Data/Laxity_0deg_VV2_kinematics_in_JCS.csv' febio_file = 'C:/oks/app/KneeHub/test/ValgusLaxity/FeBio_custom.feb' kinetics_csv = 'C:/oks/app/KneeHub/test/ValgusLaxity/Laxity_0deg_VV2_kinetics_in_TibiaCS.csv' kinematics_csv = 'C:/oks/app/KneeHub/test/ValgusLaxity/Laxity_0deg_VV2_kinematics_in_JCS.csv' make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv) def du02_calibration(): # modelprops_file = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\\Spring_Ties6\ModelProperties.xml" modelprops_file = "C:/oks/app/KneeHub/DU02/recalibration/InSituStrain/ModelProperties.xml" # AnteriorLaxity # febio_file = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\\Spring_Ties6\AnteriorLaxity\FeBio_custom.feb" # kinetics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\AP1_kinetics_in_TibiaCS.csv" # kinematics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\AP1_kinematics_in_JCS.csv" febio_file = "C:/oks/app/KneeHub/DU02/recalibration/InSituStrain/AnteriorLaxity/FeBio_custom.feb" kinetics_csv = "C:/oks/app/KneeHub/DU02/recalibration/InSituStrain/AnteriorLaxity/Laxity_0_AP1_kinetics_in_TibiaCS.csv" kinematics_csv = "C:/oks/app/KneeHub/DU02/recalibration/InSituStrain/AnteriorLaxity/Laxity_0_AP1_kinematics_in_JCS.csv" make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv, relative=False) # #PosteriorLaxity # febio_file ="C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\\Spring_Ties4\PosteriorLaxity\FeBio_custom.feb" # kinetics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_7deg_AP2_kinetics_in_TibiaCS.csv" # kinematics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_7deg_AP2_kinematics_in_JCS.csv" # # make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv, relative=False) # # #VarusLAxity # febio_file ="C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\\Spring_Ties4\VarusLaxity\FeBio_custom.feb" # kinetics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_11deg_VV2_kinetics_in_TibiaCS.csv" # kinematics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_11deg_VV2_kinematics_in_JCS.csv" # # make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv, relative=False) # # #ValgusLaxity # febio_file ="C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\\Spring_Ties4\ValgusLaxity\FeBio_custom.feb" # kinetics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_9deg_VV1_kinetics_in_TibiaCS.csv" # kinematics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\DataProcessing\Processed_Data\Laxity_9deg_VV1_kinematics_in_JCS.csv" # # make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv, relative=False) def read_from_xml(xml_file): """ read the files from an xml file and create all necessary models""" file_tree = et.parse(xml_file) file_info = file_tree.getroot() gen_files = file_info.find("general_files") feb_file = gen_files.find("febio_file").text mod_props_file= gen_files.find("model_properties_file").text processed_data_folder = gen_files.find("processed_data_directory").text models = file_info.find("Models") for mod in models: if mod.tag is et.Comment: continue model_name = mod.attrib["name"] try: kinetics_csv = os.path.join(processed_data_folder, mod.find("kinetics_csv").text) kinematics_csv = os.path.join(processed_data_folder, mod.find("kinematics_csv").text) make_model(mod_props_file, feb_file, kinetics_csv, kinematics_csv, name=model_name) except: print("failed creating model " + model_name) if __name__ == '__main__': # # to run for a single file, change the file paths here and run # modelprops_file = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\Spring_Ties2\ModelProperties.xml" # febio_file = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\Spring_Ties2\AnteriorLaxity\Test\FeBio_custom.feb" # kinematics_csv ="C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\Spring_Ties2\AnteriorLaxity\Test\Laxity_9deg_AP1_kinematics_in_JCS.csv" # kinetics_csv = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\InSituStrain\Spring_Ties2\AnteriorLaxity\Test\Laxity_9deg_AP1_kinetics_in_TibiaCS.csv" # make_model(modelprops_file, febio_file, kinetics_csv, kinematics_csv, relative=False) # to run on a bunch of models, use the exp_to_model_xml with the read_from_xml function # exmaple xml file https://simtk.org/svn/openknee/app/KneeHub/oks003/calibration/CustomizedFullModels/ExperimentalLoading02/Exp_to_Mod.xml # exp_to_model_xml = "C:\\Users\schwara2\Documents\Open_Knees\du02_calibration\CustomizedFullModels\ExperimentalLoading03\Exp_to_Mod.xml" # read_from_xml(exp_to_model_xml) # i used these helper functions when preparing models for calibration phase, # but I think it would be easier to do using an Exp_to_model_xml file oks003_calibration() #du02_calibration()