mrrezaie

# Written by Mohammadreza Rezaie [kernalnt@gmail.com]

import org.opensim.utils as utils
import os 

folder = utils.FileUtils.getInstance().browseForFolder('Choose a dirctory where STO/MOT files are located',0)

if folder==None:
	print('Nothing was selected')

mrrezaie

from matplotlib import pyplot as plt
import numpy as np
from sklearn.neighbors import KDTree

def LyE(vec, dim, lag, fs, scale=None, theiler='meanPeriod', ws=10, neighbors=1, plot=False):
    '''max lyapunov exponent (local dynamic stability) according to Rosenstein (1993) algorithm.
    https://github.com/SjoerdBruijn/LocalDynamicStability
    vec:        1D vector
    dim:        embedding dimention
    lag:        time delay

mrrezaie

import numpy as np
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances
from scipy import stats

def RQA(vec1, dim, lag, cross=None, scale=None, norm='euclidean', rescale=None, threshold=['rec',0.05], theiler=None, minLine=2, epoch=None, shift=None, get=False, save=True, show=False, plot=False, output='RQAepoch.txt'):
    '''https://en.wikipedia.org/wiki/Recurrence_quantification_analysis#RQA_measures
    https://juliadynamics.github.io/DynamicalSystems.jl/latest/rqa/quantification/
    
    diagonal                    vertical

mrrezaie

import numpy as np


def normalize(vec, typ=None):
    '''
    normalize or scale data
    typ:                        description:
    ====                        ============
    'None'                      do nothing
    'range'  ** [a,b]           ranged between a and b (e.g. [-1,1])

mrrezaie

import numpy as np
from matplotlib import pyplot as plt
from scipy import signal

def PSD(data, fs, first=None, last=None, plot=False, statistics=False):
    '''Power Spectral Density
    fs:         frequency sampled

    The period is the time duration of one cycle.
    MNF is an average frequency which is calculated as the sum of product of the EMG power spectrum and the frequency divided by the total sum of the power spectrum.

mrrezaie

import numpy as np

def SSR(data, dim, lag, plot=False):
    '''State Space Reconstruction
    represents all possible states of a system
    '''
    assert isinstance(data, np.ndarray), 'file type is not ndarray'

    if data.ndim == 1:
        data = data.reshape(-1,1)

mrrezaie

import numpy as np

def readExp(file, sep='\t'):
	''' Read OpenSim STO and MOT files 
	or any other format that the headers are separated from labels and data by 'endheader' line.
	can return data and labels, dataFrame, or dict'''
    with open(file, mode='r') as f:
        while True:
            line = f.readline()
            if 'endheader' in line:

mrrezaie

% function struct = readExp(file)
function [data, label] = readExp(file)
%Read OpenSim STO and MOT files Or any other format that 
%the headers are separated from labels and data by 'endheader' line.
%can also return struct'''

i = 0
while true
    try 
        data = dlmread(file, '\t', i, 0);

mrrezaie

from sklearn.neighbors import KDTree
import numpy as np


def sampEnt(vec1, cross=None, scale=None, dim=3, radius=0.25, lag=1, norm='chebyshev'):
    '''
    scale:      normalize input vector
                [None]: no normalization
                ['range', [0,1]]   : amplitude normalization [0,1]
                ['range', [-1,1]]   : amplitude normalization [-1,1]

mrrezaie

% Calculate free moment from Kistler (type 3) force plate
% Input is a text file contains analog signals (8 channels for each force plate)
% REF: http://isbweb.org/software/movanal/vaughan/kistler.pdf
clear all; clc; close all

i = 0;
while true
    try
        data = dlmread('atena_zarei_GR1.txt', '\t', i, 1);
%         'atena_zarei_GR1.txt'