README for data in Predictive Prosthetic Socket Design: Part 2 - generating person-specific candidate designs using multi-objective Genetic Algorithms Dataset DOI: https://doi.org/10.5258/SOTON/D0980 This dataset supports the publication: "Predictive Prosthetic Socket Design: Part 2— generating person-specific candidate designs using multi-objective Genetic Algorithms" in Biomechanics and Modeling in Mechanobiology" Author: J. W. Steer Date: 19 June 2019 This contains instructions for opening the .pkl file containing the data behind the figures. The .pkl file is strutured using dictionaries Opening the data file --------------------- The data file is encoded using the pickle package in python 3.6. The data is all stored within numpy arrays. The authors recommend downloading the anaconda distribution of python. """ import pickle import numpy with open('data.pkl', 'rb') as f: Data = pickle.load(f) """ Figure 3a -------- All individuals vs Pareto Front data = ['Individuals', 'Pareto Front'] Example Data['Figure 3a']['individuals'] Returns a numpy array of the individuals Figure 3b -------- Comparison of different GAs data = ["NSGAII", "MTS", "HEIA", "MOEAD", "IGD", "cMLGSA"] Example Data['Figure 3b']['NSGAII'] Returns a numpy array of the PF for NSGAII Figure 3c -------- Analysis of bias along the Pareto Front data = ['PF', 'bias'] Example Data['Figure 3c']['bias'] Returns a numpy array of the bias Figure 3d -------- Analysis of Pareto Front data = ['Virtual Person A', 'Virtual Person B', 'Virtual Person C', 'Virtual Person D'] Example Data['Figure 3d']['Virtual Person A'] Returns a numpy array of the PF for Virtual Person A Figure 4 -------- Optimal socket designs data = ['Virtual Person A', 'Virtual Person B', 'Virtual Person C', 'Virtual Person D'] bias = ['FF1', 'neutral', 'FF2'] ['Socket', 'Limb'] Example Data['Figure 4']['Virtual Person A']['FF1']['Socket'] Returns an Nx2 numpy array of the socket verticies and corresponding rectification Data['Figure 4']['Virtual Person A']['FF1']['Socket'] Returns an Nx2 numpy array of the limb verticies and corresponding pressure values Figure 5 -------- Progression along PF data = ['Virtual Person A', 'Virtual Person B', 'Virtual Person C', 'Virtual Person D'] Example Data['Figure 5']['Virtual Person A'] Returns an Nx7 numpy array of the bias and 6 socket design variables bias, prox, mid, dist, pat ten, fib head, tib crest Figure 6a -------- Achieved vs real PF data = ['Achieved', 'Real'] Example Data['Figure 6a']['Achieved'] Returns a numpy array of the achieved PF Figure 6a -------- Performance vs Number iterations Example Data['Figure 6b'] Returns a Nx2 numpy array of number iter and performance Licence: CC BY Related projects: EP/M508147/1; Royal Academy of Engineering (RF/130); EP/N02723X/1