python string encryption, decryption - example code

from cryptography.fernet import Fernet

def encrypt(message: bytes, key: bytes):
    return Fernet(key).encrypt(message)

def decrypt(token: bytes, key: bytes):
    return Fernet(key).decrypt(token)

key = Fernet.generate_key()  # store in a secure location
#ex) key is 'Fn1dPza4Gchl7KpPE4kz2oJEMFXYG39ykpSLcsT1icU='

message = 'This is scret string'
#encryption
enstr = encrypt(message.encode(), key)
#decryption
destr = decrypt(enstr, key).decode()

print('input:',  message)
print('encryption:', enstr)
print('decryption:', destr)

get similarity between two graphs

Basically, this example use networkX python library.
I made very simple two graphs which are G1, G2

Let see here:

and nx.graph_edit_distance this function calculate how much edit graph can be became isomorphic, that is return value of the function.

Check the example code.

..

#https://stackoverflow.com/questions/11804730/networkx-add-node-with-specific-position

#https://stackoverflow.com/questions/23975773/how-to-compare-directed-graphs-in-networkx

import matplotlib.pyplot as plt

import networkx as nx

G1=nx.Graph()

G1.add_node(1,pos=(1,1))

G1.add_node(2,pos=(2,2))

G1.add_node(3,pos=(3,1))

G1.add_edge(1,2)

G1.add_edge(1,3)

pos=nx.get_node_attributes(G1,'pos')

plt.figure('graph1')

nx.draw(G1,pos, with_labels=True)

G2=nx.Graph()

G2.add_node(1,pos=(10,10))

G2.add_node(2,pos=(20,20))

G2.add_node(3,pos=(30,10))

G2.add_node(4,pos=(40,30))

G2.add_edge(1,2)

G2.add_edge(1,3)

G2.add_edge(1,4)

pos2=nx.get_node_attributes(G2,'pos')

plt.figure('b')

nx.draw(G2,pos2, with_labels=True)

dist = nx.graph_edit_distance(G1, G2)

print(dist)

plt.show()

..

compare text using fuzzy wuzzy in python

just refer to this example..it's simple and very useful.

#pip install fuzzywuzzy

from fuzzywuzzy import process

candidate = ["Atlanta Falcons", "New York Jetss", "New York Giants", "Dallas Cowboys"]

search = "new york jets"

r1 = process.extract(search, candidate)

#r1 = process.extract(search, candidate, limit=3)

search = "cowboys"

r2 = process.extractOne(search, candidate)

search = "new york jets"

r3 = process.extractBests(search, candidate, score_cutoff=70)

print(r1)

#[('New York Jetss', 96), ('New York Giants', 79), ('Atlanta Falcons', 29), ('Dallas Cowboys', 22)]

print(r2)

#('Dallas Cowboys', 90)

print(r3)

#[('Dallas Cowboys', 90)]