rwarren

#!/usr/bin/env python
# THIS FILE IS A MAAAAAAAAAAAASSIVE HACK QUICKLY MADE FOR THE ONLINE PGCON_2020
# There is DEFINITELY a better way to do this.

import argparse
import datetime
import sched
import socket
import sys
import time

rwarren

[global_tags]
  srv_loc="home"
  srv_vtype="real"
  srv_purpose="chia-plotter"
  srv_owner="internal"

[agent]
  interval = "60s"
  round_interval = true
  metric_batch_size = 1000

rwarren

#!/usr/bin/env python3
import os
from pprint import pprint as pp
import shutil
import socket
import sys
import typing as th


# Largest plot size seen is 108,935,751,977, and smallest is 108,232,347,648. For

rwarren

def daysToWin(netspaceFraction, confidence = 0.95, challengesPerDay = 4608):
    losingProbability = 1 - netspaceFraction
    challengesToWin = math.log(1 - confidence, losingProbability)
    return challengesToWin / challengesPerDay

def earningsPerYear(xchValue_USD, netspaceFraction, confidence = 0.95, challengesPerDay = 4608, xchPerWin = 2):
    xchPerDay = xchPerWin / daysToWin(netspaceFraction, confidence, challengesPerDay)
    dailyUSD = xchValue_USD * xchPerDay
    return dailyUSD * 365

rwarren

import pathlib
import yaml

def _path_representer(dumper, data):
    return dumper.represent_scalar("!Path", str(data))

def _path_constructor(loader, node):
    value = loader.construct_scalar(node)
    return pathlib.Path(value)