asherp

# -*- encoding: utf-8 -*-
# modified for python3 https://stackoverflow.com/a/48806794/2793333

import os
import fileinput
import hashlib
import random
from ipython_genutils.py3compat import cast_bytes, str_to_bytes

# Get the password from the environment

asherp

NFTs: A Bitcoiner's Perspective

Non-fungible tokens (NFTs) are becoming a popular way for artists to monetize their content. Recently, many bitcoiners have maligned the model as illigitimate, calling them scams or the like. As someone who attempted the same model with colored coins in 2012, I would have thought bitcoiners would understand why this model not only fits within the ethos of bitcoin, but is very likely to succeed for similar reasons. I believe success in the NFT model only improves the likelihood of mainstream success of bitcoin. Ironically, bitcoin influencers are using the same arguments made against bitcoin to denounce NFTs. Let's examine each of these arguments and how they have been applied to bitcoin in the past. My hope is that this will sway bitcoiners to not write them off as a mere phase akin to ICOs, but as a growing part of the virtual economy and a sign of things to come.

Addressing Technical Concerns

Technical implementations are not the issue

Some bitcoiners take issue wi

asherp

"""Original Code from Tim Wylie http://www.cs.montana.edu/~timothy.wylie/frechet/ 
A. Pembroke added modifications to combine frechet distance with closest ordered pairs
and implemented an iterative version of the original recursive discrete frechet calculation, df_iterative.
"""

##############################################################################################################
##############################################################################################################
## File: fpact_frechet.py
## Author: Tim Wylie
## Copyright: Tim Wylie

asherp

#!/usr/bin/python
# Derived from: https://github.com/KasparP/PSI_simulations/blob/master/Python/SLAPMi/lsqlin.py
# See http://maggotroot.blogspot.ch/2013/11/constrained-linear-least-squares-in.html for more info
'''
    A simple library to solve constrained linear least squares problems
    with sparse and dense matrices. Uses cvxopt library for
    optimization
'''
__author__ = 'Valeriy Vishnevskiy'
__email__ = 'valera.vishnevskiy@yandex.ru'

asherp

def windowed_time_average(data, window = 3):
    window = 3
    results = []
    for i in range(len(data)):
        if i < window:
            results.append(np.sum(data[:i+1])/window)
        else:
            results.append(np.sum(data[i-window+1:i+1])/window)
    return np.array(results)

asherp

import dash
from dash.dependencies import Input, Output, State, Event
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go


import numpy as np

class dccType(object):

asherp

import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html

class dccType(object):
    """
    Object-Orient dash component
    """
    def __init__(self, divtype, component_property='value', string=str, **kwargs):

asherp

'''Bezier interpolation method based on 
	http://www.antigrain.com/research/bezier_interpolation/

'''
import numpy as np
import plotly.graph_objs as go
import colorlover as cl

def roll_forward(a, steps = 1, axis = 0):
	return np.roll(a, steps, axis = axis)

asherp

<link rel="import" href="../polymer/polymer.html">

<polymer-element name="my-element">

  <template>
    <style>    
      #design_host {
        position: absolute;
        width: 100%;
        height: 100%;

asherp

<link rel="import" href="../polymer/polymer.html">

<polymer-element name="my-element">

  <template>
    <style>    
      #design_host {
        position: absolute;
        width: 100%;
        height: 100%;