<jupyter_start><jupyter_text><jupyter_code># andy 
# Simplicial complexex form distance matrice
# from matrice CS .
import pandas as pd
import numpy as np
import gudhi as gd
from pylab import*
from  google.colab import drive 
drive.mount("/content/drive")
#montar mi drive para los archivos 
data = pd.read_csv("/content/drive/My Drive/data-tda/distancia_total.csv", sep = "," , engine="python")
data.head
#correlacion de los datos de la matriz
data.corr
data_corr  = pd.DataFrame(data)
data_corr.head
# Rips
# Generar el esqueleto
#El parámetro max_edge_length es el diámetro máximo
skeleton_data = gd.RipsComplex(distance_matrix = data_corr.values,max_edge_length = 0.8)
Rips_simplex_tree_<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09153952843273232, 'fraction_numerical': 0.0027739251040221915, 'mean_word_length': 5.747663551401869, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '590830', 'license': 'Apache-2.0', 'n_tokens_mistral': 270, 'n_tokens_neox': 261, 'n_words': 69, 'path': 'simplicial_complexes_distance.ipynb', 'repo_name': 'andresvillamayor/TDA-public', 'size': '721'}