import sys import numpy as np import os import pandas as pd import copy from lxml import etree as et import matplotlib.pyplot as plt import csv from matplotlib import gridspec import LogPostProcessing def experiment_data(experiment_csv): try: df_experiment = pd.read_csv(experiment_csv, encoding='utf7', na_values='nan') print("experiment data read") except: df_experiment = pd.read_csv(experiment_csv, encoding='utf8', na_values='nan') print("experiment data read") experiment_data = [] for n in df_experiment.columns: experiment_data.append(np.asarray(list(map(float, df_experiment[n].values)))) if "ImageCS" in experiment_csv: experiment_data = experiment_data[1:] data_names = df_experiment.columns[1:] experiment_time_steps = np.array(range(len(experiment_data[0]))) experiment_time_steps = experiment_time_steps/max(experiment_time_steps) else: applied_load = experiment_data[1] experiment_data = experiment_data[2:] data_names = df_experiment.columns[2:] # the time steps are based on the normalized applied load # max_arg = np.nanargmax(np.abs(applied_load)) # experiment_time_steps = applied_load / applied_load[max_arg] experiment_time_steps=applied_load experiment_data = np.asarray(experiment_data) # check that all data is in N, Nm, mm, deg. for plotting .change if necessary for i, name in enumerate(data_names): if "[m]" in name or "(m)" in name: experiment_data[i] = 1000 * experiment_data[i] elif "[rad]" in name or "(rad)" in name: experiment_data[i] = np.degrees(experiment_data[i]) elif "[Nmm]" in name or "(Nmm)" in name: experiment_data[i] = experiment_data[i]/1000 if "kinetics" in experiment_csv.lower(): names = ["Medial_Load", "Anterior_Load", "Superior_Load", "Extension_Moment", "Valgus_Moment", "External_Moment"] else: names = ["Medial_Translation","Anterior_Translation","Superior_Translation","Extension_Rotation","Valgus_Rotation","External_Rotation"] return experiment_time_steps, experiment_data, names def read_raw_data(csv): try: df_experiment = pd.read_csv(csv, encoding='utf7', na_values='nan') print("experiment data read") except: df_experiment = pd.read_csv(csv, encoding='utf8', na_values='nan') print("experiment data read") raw_data = [] for n in df_experiment.columns: raw_data.append(np.asarray(list(map(float, df_experiment[n].values)))) time_steps = raw_data[1] raw_data = raw_data[2:] data_names = df_experiment.columns[2:] time_steps = np.asarray(time_steps) raw_data= np.asarray(raw_data) return time_steps, raw_data, data_names def Raw_Data(csv_file, title): """load raw csv files and make graphs""" time, data, names = read_raw_data(csv_file) if "kinetics" in csv_file.lower(): trans_title = 'Load [N]' rot_title = 'Torque [Nm]' else: trans_title = 'Translation [mm]' rot_title = 'Rotation [deg]' fig = plt.figure(figsize=(12,8)) ttl = fig.suptitle(title, fontsize=16) trans_ax = plt.subplot2grid((1, 2), (0, 0)) rot_ax = plt.subplot2grid((1, 2), (0, 1)) colors= ['r','b','g','c','m','y'] mins = [] maxs = [] for a in range(3): ax = trans_ax ax.plot(time, data[a], label=names[a], color=colors[a]) mins.append(np.nanmin(data[a])) maxs.append(np.nanmax(data[a])) ax.grid('on') trans_handles, trans_labels = ax.get_legend_handles_labels() ax.set_xlabel('Time (ms)') ax.set_title(trans_title) ax.legend(loc='best') mins = [] maxs = [] for a in range(3, 6): ax = rot_ax ax.plot(time, data[a], label=names[a], color=colors[a]) mins.append(np.nanmin(data[a])) maxs.append(np.nanmax(data[a])) ax.grid('on') rot_handles, rot_labels = ax.get_legend_handles_labels() ax.set_xlabel('Time (ms)') ax.set_title(rot_title) ax.legend(loc='best') # # h = trans_handles + rot_handles # l = trans_labels + rot_labels # # lgd = fig.legend(h, l, loc='upper center', bbox_to_anchor=(1.05, 0.6)) fig.savefig(title) plt.show() def Processed_PF(csv_file, title): time, data, names = experiment_data(csv_file) # remove nans from data and time where_nan = np.isnan(data[0]) data = data[:,~where_nan] time= time[~where_nan] if "kinetics" in csv_file.lower(): trans_title = 'Load [N]' rot_title = 'Torque [Nm]' else: trans_title = 'Translation [mm]' rot_title = 'Rotation [deg]' fig = plt.figure(figsize=(12, 8)) ttl = fig.suptitle(title, fontsize=16) trans_ax = plt.subplot2grid((1, 2), (0, 0)) rot_ax = plt.subplot2grid((1, 2), (0, 1)) colors = ['r', 'b', 'g', 'c', 'm', 'y'] mins = [] maxs = [] for a in range(3): ax = trans_ax ax.plot(time, data[a], 'o', label=names[a], color=colors[a]) ax.plot(time, data[a], color=colors[a]) mins.append(np.nanmin(data[a])) maxs.append(np.nanmax(data[a])) ax.grid('on') trans_handles, trans_labels = ax.get_legend_handles_labels() ax.set_xticklabels([]) ax.set_title(trans_title) ax.legend(loc='best') mins = [] maxs = [] for a in range(3, 6): ax = rot_ax ax.plot(time, data[a], 'o', label=names[a], color=colors[a]) ax.plot(time, data[a], color=colors[a]) mins.append(np.nanmin(data[a])) maxs.append(np.nanmax(data[a])) ax.grid('on') rot_handles, rot_labels = ax.get_legend_handles_labels() ax.set_xticklabels([]) ax.set_title(rot_title) ax.legend(loc='best') # # h = trans_handles + rot_handles # l = trans_labels + rot_labels # # lgd = fig.legend(h, l, loc='upper center', bbox_to_anchor=(1.05, 0.6)) fig.savefig(title) plt.show() def Processed_Laxity(files, title): """load the csv files from oks processed data and make graphs. files = [A,P,VR,VL,IR,ER]""" # fig = plt.figure(figsize=(8, 14)) # ax1 = plt.subplot2grid((6,2),(0,0)) # ax2 = plt.subplot2grid((6,2),(0,1)) # ax3 = plt.subplot2grid((6,2),(1,0)) # ax4 = plt.subplot2grid((6,2),(1,1)) # ax5 = plt.subplot2grid((6,2),(2,0)) # ax6 = plt.subplot2grid((6,2),(2,1)) # ax7 = plt.subplot2grid((6,2),(3,0)) # ax8 = plt.subplot2grid((6,2),(3,1)) # ax9 = plt.subplot2grid((6,2),(4,0)) # ax10 = plt.subplot2grid((6,2),(4,1)) # ax11 = plt.subplot2grid((6,2),(5,0)) # ax12= plt.subplot2grid((6,2),(5,1)) # # all_axes = [(ax1,ax2),(ax3,ax4),(ax5,ax6),(ax7,ax8),(ax9,ax10),(ax11,ax12)] fig = plt.figure(figsize=(20, 5)) ax1 = plt.subplot2grid((2,6),(0,0)) ax2 = plt.subplot2grid((2,6),(1,0)) ax3 = plt.subplot2grid((2,6),(0,1)) ax4 = plt.subplot2grid((2,6),(1,1)) ax5 = plt.subplot2grid((2,6),(0,2)) ax6 = plt.subplot2grid((2,6),(1,2)) ax7 = plt.subplot2grid((2,6),(0,3)) ax8 = plt.subplot2grid((2,6),(1,3)) ax9 = plt.subplot2grid((2,6),(0,4)) ax10 = plt.subplot2grid((2,6),(1,4)) ax11 = plt.subplot2grid((2,6),(0,5)) ax12= plt.subplot2grid((2,6),(1,5)) all_axes = [(ax1,ax2),(ax3,ax4),(ax5,ax6),(ax7,ax8),(ax9,ax10),(ax11,ax12)] if "kinematics" in files[0].lower(): ax1.set_ylabel('Translation [mm]', fontsize=12) ax2.set_ylabel('Rotation [deg]', fontsize=12) else: ax1.set_ylabel('Load [N]', fontsize=12) ax2.set_ylabel('Moment [Nm]', fontsize=12) loading= ['Anterior Load [N]', 'Posterior Load [N]', 'Varus Torque [Nm]', 'Valgus Torque [Nm]', 'Internal Torque [Nm]', 'External Torque [Nm]'] colors= ['r','b','g','c','m','y'] for i in range(6): # for each loading direction trans_ax= all_axes[i][0] rot_ax = all_axes[i][1] axes = [trans_ax, trans_ax, trans_ax, rot_ax, rot_ax, rot_ax] applied_load, data, names = experiment_data(files[i]) if np.nanmean(applied_load) < 0: applied_load = -applied_load # get the positive since we are assigning direction in the x label name for a in range(6): ax = axes[a] ax.plot(applied_load, data[a], 'o', label=names[a], markersize = 3, color = colors[a]) ax.plot(applied_load, data[a],color=colors[a]) ax.grid('on') # if i==0: # if a<3: # ax.set_ylabel('Translation [mm]', fontsize=12) # else: # ax.set_ylabel('Rotation [deg]', fontsize=12) if a<3: t_handles, t_labels = ax.get_legend_handles_labels() # store handles and lables ax.set_xticklabels([]) else: r_handles, r_labels = ax.get_legend_handles_labels() if a == 5: ax.set_xlabel(loading[i], fontsize=12) # plt.tight_layout() # fig.legend(trans_handles, trans_labels, loc='upper left') # fig.legend(rot_handles,rot_labels, loc='upper right') handles = t_handles + r_handles labels = t_labels + r_labels lgd = plt.legend(handles, labels, loc='upper right', bbox_to_anchor = (2.0,2.0)) # lgd = plt.legend(handles, labels, loc='upper center', bbox_to_anchor=(1.05, 0.6)) ttl = fig.suptitle(title, fontsize=16) # text = ax.text(-0.2, 1.05, "Aribitrary text", transform=ax.transAxes) # ax.set_title("Trigonometry") fig.savefig(title, bbox_extra_artists=(lgd,ttl), bbox_inches='tight') # fig.legend(h,l,loc='upper right',bbox_to_anchor=(1.0, 0.5)) plt.show() def make_graphs(): # # 0 deg laxity kinematics # A = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_AP1_kinematics_in_JCS.csv" # P = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_AP2_kinematics_in_JCS.csv""" # VR= "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_VV1_kinematics_in_JCS.csv" # VL = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_VV2_kinematics_in_JCS.csv" # IR = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_EI2_kinematics_in_JCS.csv" # ER = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_EI1_kinematics_in_JCS.csv" # files = [A,P,VR,VL,IR,ER] # Processed_Laxity(files,"Processed 0 deg Laxity Kinematics") # # 0 deg laxity kinetics # A = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_AP1_TibiaKinetics_in_TibiaCS.csv" # P = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_AP2_TibiaKinetics_in_TibiaCS.csv""" # VR= "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_VV1_TibiaKinetics_in_TibiaCS.csv" # VL = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_VV2_TibiaKinetics_in_TibiaCS.csv" # IR = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_EI2_TibiaKinetics_in_TibiaCS.csv" # ER = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_EI1_TibiaKinetics_in_TibiaCS.csv" # files = [A,P,VR,VL,IR,ER] # Processed_Laxity(files,"Processed 0 deg Laxity Kinetics") # #0 deg laxity raw kinematics # raw_lx0 = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_Kinematics_raw.csv" # Raw_Data(raw_lx0, "Raw 0 deg Laxity Kinematics") # #0 deg laxity raw kinetics # raw_lx0 = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Laxity_0deg_Kinetics_raw.csv" # Raw_Data(raw_lx0, "Raw 0 deg Laxity Kinetics") # #passive flexion raw kinematics # raw_pf = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Passive_Flexion_Kinematics_raw.csv" # Raw_Data(raw_pf, "Raw Passive Flexion Kinematics") # #passive flexion processed kinematics # processed_pf = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Passive_Flexion_Kinematics_in_JCS.csv" # Processed_PF(processed_pf, "Processed Passive Flexion Kinematics") # #passive flexion raw kinetics # raw_pf = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Passive_Flexion_Kinetics_raw.csv" # Raw_Data(raw_pf, "Raw Passive Flexion Kinetics") # #passive flexion processed kinetics- image_CS # processed_pf = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Passive_Flexion_Kinetics_in_ImageCS.csv" # Processed_PF(processed_pf, "Processed Passive Flexion Kinetics") #passive flexion processed kinetics - tibia CS processed_pf = "C:\\Users\schwara2\Documents\Open_Knees\oks003_calibration\DataProcessing\Processed_Data\Passive_Flexion_Kinetics_in_TibiaCS.csv" Processed_PF(processed_pf, "Processed Passive Flexion Kinetics") if __name__=="__main__": make_graphs()