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@tudorelu
tudorelu / geojsonToTiles.js
Last active May 20, 2018 01:38 — forked from kekscom/gist:ac499df64c8d861776a61031d577d8bf
Split huge GeoJSON files into tiles
var tileSize = 256;
var zoom = 16;
var srcFile = 'huge.geojson';
var dstPath = 'tiles/';
var minX = Number.MAX_VALUE;
var maxX = 0;
var minY = Number.MAX_VALUE;
var maxY = 0;
./ethdcrminer64 -epool asia1.ethermine.org:4444 -ewal 0xD95C02d461f6b1FaC5AFDE929D9Bc92611472081 -epsw x -dpool stratum+tcp://dcr.suprnova.cc:3252 -dwal doornumber2.dw1 -dpsw dw1pass -dcri 20 -tt 70 -tstop 85 -r 1

"Pull while keeping your changes intact"

git pull -s recursive -X ours

"Reset the working tree to the last commit"

git reset --hard HEAD

@tudorelu
tudorelu / gist:fdd155051480f78c906b38799b5aa199
Last active June 15, 2018 02:02
Ubuntu 16.04 AMD Miner setup
#On a clean install of Ubuntu 16.04, how to get the best hashrate out of your cards (prior to OCing and BIOS flashing)
sudo apt update
sudo apt dist-upgrade
#Install version 17.40 of the AMDGPU-PRO Linux Drivers:
cd /opt
sudo wget --referer=http://support.amd.com https://www2.ati.com/drivers/linux/ubuntu/amdgpu-pro-17.40-492261.tar.xz
sudo tar -Jxvf amdgpu-pro*
cd amd*
@tudorelu
tudorelu / TudorCCC.md
Last active March 12, 2019 03:24
Tudor's CCC proposal

Tudor's CCC Proposal

General Information

Name Tudor Barbulescu

Location Canberra, Australia

using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.EventSystems;
using UnityEngine.XR.ARFoundation;
/// <summary>
/// Controls the basketball.
/// </summary>
[RequireComponent(typeof(Rigidbody))]
admin.addPeer(""enode://50c5f812da8c8eab832fa29258e683132b36ace28f9fe3be908e5ff33981f27dcaa13901089827f22a3d8693fd6e6cecdf629683fdde32695bd2ae1961a7dab5@203.214.112.190:30303?discport=0")
import RPi.GPIO as GPIO
import time
import math
import sys
from hx711 import HX711
from pijuice import PiJuice
import os
import firebase_admin
@tudorelu
tudorelu / all_indicators.py
Created July 12, 2020 02:29
the list of all on chain data points
all_endpoints = dict(
indicators_sopr = "indicators/sopr",
indicators_asopr = "indicators/sopr_adjusted",
indicators_lth_sopr = "indicators/sopr_more_155",
indicators_sth_sopr = "indicators/sopr_less_155",
indicators_nvt = "indicators/nvt",
indicators_nvts = "indicators/nvts",
indicators_velo = "indicators/velocity",
indicators_cdd = "indicators/cdd",
indicators_sacdd = "indicators/cdd_supply_adjusted",
Documentation here:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corrwith.html
Using Pearson Correlation
from wikipedia:
In statistics, the Pearson product-moment correlation coefficient is a
measure of the linear dependence between two variables X and Y, giving
a value between +1 and −1 inclusive, where 1 is total positive linear
correlation, 0 is no linear correlation, and −1 is total negative linear