TDA and Statistics using Gudhi Python Library

Part 2 - The bottleneck distance

In this second part of the tutorial we compute bottleneck distances between persistence diagrams.

In [1]:
from IPython.display import Image
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import numpy as np
import pandas as pd
import pickle as pickle
import gudhi as gd
from pylab import *
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from IPython.display import Image
from sklearn import manifold
%matplotlib inline

We consider the fourteen MBP sructures, we compute the matrix of distances associated to each configuration:

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path_file = "./Peter corr_ProteinBinding/"
files_list = [
corr_list = [pd.read_csv(path_file+u , header=None,delim_whitespace=True) for u in files_list]
dist_list = [1- np.abs(c) for c in corr_list]

Exercice. Compute and store in a list the fourteen Rips complex filtrations.

Bottleneck distance

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