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
Image("SlidesGudhi/GeneralPipeLine_bottleneck.png")
Out[1]:
In [ ]:
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:

In [ ]:
path_file = "./Peter corr_ProteinBinding/"
files_list = [
'1anf.corr_1.txt',
'1ez9.corr_1.txt',
'1fqa.corr_2.txt',
'1fqb.corr_3.txt',
'1fqc.corr_2.txt',
'1fqd.corr_3.txt',
'1jw4.corr_4.txt',
'1jw5.corr_5.txt',
'1lls.corr_6.txt',
'1mpd.corr_4.txt',
'1omp.corr_7.txt',
'3hpi.corr_5.txt',
'3mbp.corr_6.txt',
'4mbp.corr_7.txt']
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

In [2]:
Image("SlidesGudhi/Bottleneck0.png")
Out[2]: