ehoppmann

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// @author       ehoppmann
// @copyright    2021, ehoppmann (https://gist.github.com/ehoppmann)
// @license      MIT
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ehoppmann

Workarounds for Netflix and the blocking of Hurricane Electric IPv6 tunnels

The dreaded "You seem to be using an unblocker or proxy." error message. Cool story bro.

This gist was essentially created out of my own rant about Netflix being hostile to IPv6 tunnel services since June 2016. You are welcome to read my opinion on the matter, this is the more technical side to the issue and how to combat it within your own network.

Since I wrote this, various GitHub users have contributed their thoughts and ideas which has been incorporated into this gist. Thank you to everyone who have contributed their own methods and implementations.

The problem

Netflix now treats IPv6 tunnel brokers (such as Hurricane Electric) as proxy servers. A while ago it became apparent to users and Netflix that somewhat by accident, IPv6 tunnel users were being served content outside of their geolocation because of the way Netflix was identifyi

ehoppmann

THIS ARE MY NOTES OF BUILDING AN INSTALLING XORGXRDP AND XRDP WITH GPU ACCELERATION
TESTED ON UBUNTU 20.04.2 with amdgpu (all-open variant) driver and FirePro W4100 GPU

-- Build XorgXrdp with GPU acceleration ("script" - to be adjusted to your needs) : --

## << BUILD AND INSTALL SCRIPT START >> ##
#!/bin/bash
# Install Latest XRDP with XORGXRDP

# README

ehoppmann

#!/usr/bin/env python3
# based on https://github.com/cherrypy/cherrypy/blob/0857fa81eb0ab647c7b59a019338bab057f7748b/cherrypy/process/wspbus.py#L305
import sys
import os
import time

_startup_cwd = os.getcwd()

def _do_execv():
    args = sys.argv[:]

ehoppmann

Kimbie

#F3E3CD,#F3E3CD,#F3951D,#DA3D61,#F26328,#183E1C,#DA3D61,#F26328

Monokai

#222222,#2F2F2F,#F92772,#FFFFFF,#A6E22D,#FFFFFF,#66D9EF,#BE84F2

Juice Bar

#86A34E,#94AF63,#FFFFFF,#6D8B42,#94AF63,#FFFFFF,#FFB10A,#DFA044

ehoppmann

from __future__ import division
import numpy as np
import scipy.stats.kde as kde

def hpd_grid(sample, alpha=0.05, roundto=2):
    """Calculate highest posterior density (HPD) of array for given alpha. 
    The HPD is the minimum width Bayesian credible interval (BCI). 
    The function works for multimodal distributions, returning more than one mode

    Parameters

ehoppmann

# -*- coding: utf-8 -*-
# -*- mode: python -*-
# Adapted from mpl_toolkits.axes_grid1
# LICENSE: Python Software Foundation (http://docs.python.org/license.html)

from matplotlib.offsetbox import AnchoredOffsetbox
class AnchoredScaleBar(AnchoredOffsetbox):
    def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
                 pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None, 
                 **kwargs):

ehoppmann

COST_GB_SEC = 0.00001667

cost = 0
with open('logs.log', 'r') as f:
    logs = f.readlines()
billing = [i.split('\t') for i in logs if '\tBilled Duration:' in i]

for i in billing:
    gb = float(i[3].lstrip('Memory Size: ').rstrip(' MB')) / 1024
    sec = float(i[2].lstrip('Billed Duration: ').rstrip(' ms ')) / 1000

ehoppmann

from typing import Iterable, Callable, List

def ordered_set(iterable: Iterable, key: Callable=lambda x: x) -> List:
    """
    Returns a new list containing all unique items from the iterable. A custom key function can
    be specified to customize the uniqueness constraint, for example to return the first unique
    element from the input list based on the second item in each element, one could pass
    `lambda x: x[1]`.
    """
    out_list = []

ehoppmann

"""Plots a Pandas dataframe as a heatmap"""
import matplotlib as mpl
import matplotlib.pyplot as plt

def heatmap(df,
            edgecolors='w',
            cmap=mpl.cm.RdBu,
            log=False,vmin=0,vmax=500):    
    width = len(df.columns)/4
    height = len(df.index)/4