mcdallas

#!/usr/bin/env python3

import json
import base64
import argparse
from enum import Enum

"""

    Usage:

mcdallas

import requests
import hashlib

def is_password_pwned(password):
    """Securely check if a password has been part of a known breach by transmiting only
    the first 5 digits of it's hash to api.pwnedpasswords.com """

    phash = hashlib.sha1(password.encode()).hexdigest().upper()

    url = f"https://api.pwnedpasswords.com/range/{phash[:5]}"

mcdallas

from itertools import cycle

cipher = [int(i) for i in input('Enter ciphertext:\n').split()]

splits = [cipher[::3], cipher[1::3], cipher[2::3]]


def distance_mod_256(x, y):
    return min((x - y) % 256, (y - x) % 256)

mcdallas

sudo apt-get install libssl-dev
sudo apt-get install gcc
sudo apt-get install zlib1g-dev

cd /opt
sudo wget https://www.python.org/ftp/python/3.6.5/Python-3.6.5.tgz

sudo tar -xvf Python-3.6.5.tgz

cd Python-3.6.5

mcdallas

"""Generic linux daemon base class for python 3.x."""

import sys, os, time, atexit, signal

class Daemon:
	"""A generic daemon class.

	Usage: subclass the daemon class and override the run() method."""

	def __init__(self, pidfile): 

mcdallas

Keybase proof

I hereby claim:

• I am mcdallas on github.
• I am mcdallas (https://keybase.io/mcdallas) on keybase.
• I have a public key ASAzVnWReYKh-qPba_e25kweyoGMr6GI-fdXdgr1Uywh8wo

To claim this, I am signing this object:

mcdallas

import hashlib
from datetime import datetime
import pprint

class Block:
    def __init__(self, index, timestamp, data, previous_hash):
        self.index = index
        self.timestamp = timestamp
        self.data = data
        self.previous_hash = previous_hash

mcdallas

04c74c268f5d9335e687cf7186f482564ce1f514e92af1ea5b4fb8f878783caad38e5b11660db81e366faaf0829c3a170a9495e6964244659bf7e6dffac00c7950;k0nstantin0s

mcdallas

extern crate num;
use num::bigint::BigInt;
use num::FromPrimitive;
use num::Integer;
use num::One;
use num::Zero;


#[derive(PartialEq, Clone)]
struct Point {

mcdallas

import numpy as np

def estimate_pi(z):
    x = np.random.uniform(low=-1, high=1, size=z)
    y = np.random.uniform(low=-1, high=1, size=z)
    return (x**2 + y**2 < 1).sum()/z*4