| """ | |
| Python code for fractional differencing of pandas time series | |
| illustrating the concepts of the article "Preserving Memory in Stationary Time Series" | |
| by Simon Kuttruf | |
| While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code. | |
| """ | |
| import numpy as np | |
| import pandas as pd |
This little script will output the amount of tokens subscribed to a topic.
You will need to replace the variables with your personal ones in order for it to work.
We used this function in a Lambda which sends the results to a monitoring dashboard.
Screenshots in the comments.
25/5/2020
Imagine a future where a user Alice has bitcoins and wants to send them with maximal privacy, so she creates a special kind of transaction. For anyone looking at the blockchain her transaction appears completely normal with her coins seemingly going from address A to address B. But in reality her coins end up in address Z which is entirely unconnected to either A or B.
Now imagine another user, Carol, who isn't too bothered by privacy and sends her bitcoin using a regular wallet which exists today. But because Carol's transaction looks exactly the same as Alice's, anybody analyzing the blockchain must now deal with the possibility that Carol's transaction actually sent her coins to a totally unconnected address. So Carol's privacy is improved even though she didn't change her behaviour, and perhaps had never even heard of this software.
| import argparse | |
| import requests | |
| def get_allocation(address, blockchain): | |
| id = { | |
| "sol": 1, | |
| "eth": 2, | |
| "sui": 21, | |
| "aptos": 22, | |
| "inj": 19, |