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"""
Programmer: Chris Tralie
Purpose: To demonstrate a greedy furthest point sampling, which is a general technique
for getting good points that are "spread out" and cover the dataset well
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import pairwise_distances

def getGreedyPerm(D):

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"""
Programmer: Chris Tralie
Purpose: To illustrate the dynamic time warping algorithm
to synchronize two 2D trajectories
"""
import numpy as np 
import matplotlib.pyplot as plt 

def getCSM(X, Y):
    """

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"""
Programmer: Chris Tralie
Purpose: To use the POT library (https://github.com/rflamary/POT)
to compute the Entropic regularized Wasserstein distance 
between points on a 2D grid
"""
import numpy as np
import matplotlib.pyplot as plt
import ot

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"""
Programmer: Chris Tralie ([email protected])
Purpose: To implement the connection Laplacian and show its use
for synchronizing a set of vectors on an NxN grid
"""
import numpy as np
import numpy.linalg as linalg
from scipy import sparse 
from scipy.sparse.linalg import lsqr, cg, eigsh
import matplotlib.pyplot as plt

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"""
Programmer: Chris Tralie
Purpose: To show how to plot "time-ordered point clouds"
with colors on the rainbow to indicate the time
(red beginning, magenta end)
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def getEDM(X):
    """
    Compute an all pairs distance matrix using broadcasting
    and matrix multiplications to speed up computations
    Parameters
    ----------

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import numpy as np
import scipy.io.wavfile as wavfile
import matplotlib.pyplot as plt

duration = 20
fs = 44100
y = np.zeros(fs*duration)
n_harmonics = 5
t = np.arange(fs*duration) # INCORRECT SAMPLING
#t = np.linspace(0, duration, fs*duration)  # CORRECT SAMPLING

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