motion_analysis_and_visualization_script [Emilys Galactic Gits]

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====== Motion Analysis and Visualization Script ======

A Python script that reads motion data from CSV files, computes spatial metrics like the center of position and average distance from the center over time, performs Lomb-Scargle periodogram analysis, and saves analyzed results as Numpy arrays. It utilizes libraries such as Pandas for data manipulation, Matplotlib and mplEasyAnimate for visualization, and Scipy for signal processing.

<code python>
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from tqdm import tqdm
import numpy as np
from mplEasyAnimate import animation
from scipy.signal import lombscargle
import os

def frameToArray(frame):
    start = 1
    vecs = np.zeros(shape=(int((frame.shape[0]-1)/3), 3))
    for itr, end in enumerate(range(4, frame.shape[0]+3, 3)):
        vecs[itr] = np.array(frame[start:end])
        start = end
    return vecs

def CalcCenterOfPos(frame):
    vecs = frameToArray(frame)
    return vecs.mean(axis=0)

def CenterOfPosEvolution(df):
    COPs = np.zeros(shape=(df.shape[0], 3))
    times = df['Time'].values
    for itr, frame in tqdm(df.iterrows(), total=df.shape[0]):
        COPs[itr] = CalcCenterOfPos(frame)
    return COPs, times

def getAvgPosFromCenter(frame):
    COP = CalcCenterOfPos(frame)
    vecs = frameToArray(frame)
    return np.abs(vecs-COP).mean(axis=0)

def getAvgDistFromCenter(frame):
    COP = CalcCenterOfPos(frame)
    vecs = frameToArray(frame)
    return np.mean(np.sqrt(np.sum(np.power(np.subtract(COP, vecs), 2), axis=1)))

def evolveAvgPosFromCenter(df):
    SEPs = np.zeros(shape=(df.shape[0], 3))
    times = df['Time'].values
    for itr, frame in tqdm(df.iterrows(), total=df.shape[0]):
        SEPs[itr] = getAvgPosFromCenter(frame)
    return SEPs, times

def evolveAvgDistFromCenter(df):
    SEPs = np.zeros(shape=(df.shape[0],))
    times = df['Time'].values
    for itr, frame in tqdm(df.iterrows(), total=df.shape[0]):
        SEPs[itr] = getAvgDistFromCenter(frame)
    return SEPs, times

def genLSP(t, y, s=None):
    nyquist = 1/(2*(t[1]-t[0]))
    res = (t[1]-t[0])/t.shape[0]
    if not s:
        s = int(1/res)
    f = 2*np.pi*np.linspace(res/10, 0.5, s)
    pgram = lombscargle(t, y, f, normalize=True)
    return f, pgram

if __name__ == "__main__":
    root = "Downloaded_Data/CSVs"
    files = os.listdir(root)
    files = [x for x in files if x[0] != '.']

    for file in tqdm(files):
        df = pd.read_csv('{}/{}'.format(root, file), header=6)
        df = df.rename(columns={ df.columns[0]: "Time" })
        SEPs, times = evolveAvgDistFromCenter(df)

        name = file.split('.')[0]
        np.save('AnalyzedData/AvgDistFromCenter_{}.npy'.format(name), np.vstack([times, SEPs]))

</code>