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package main | |
import ( | |
"log" | |
"github.com/ry/v8worker" | |
) | |
const OID_LOOKUP = ` | |
var oidmap = { |
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use_bpm 30 | |
live_loop :piano do | |
sample :ambi_piano, beat_strech: 2 | |
sleep 2 | |
end | |
live_loop :amen_break do | |
with_fx :slicer, phase: 0.5, wave: 0, mix: 1 do |
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#! /usr/bin/env python | |
import pyrealsense2 as rs | |
import numpy as np | |
import cv2 | |
import sys | |
import datetime | |
import os | |
import json |
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#!/usr/bin/env python | |
import numpy as np | |
from open3d import * | |
class Node: | |
def __init__(self, center = [0,0,0], size=0.0, children=[], data=None): | |
self.center = center | |
self.size = size |
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#include <librealsense2/rs.hpp> // Include RealSense Cross Platform API | |
#include <iostream> | |
#include <fstream> | |
#include <map> | |
#include <chrono> | |
#include <mutex> | |
#include <thread> | |
#include "MadgwickAHRS.h" // See: http://www.x-io.co.uk/node/8#open_source_ahrs_and_imu_algorithms | |
#include <Eigen/Geometry> | |
#include <GL/glut.h> |
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import numpy as np | |
AC_EQUITIES = "Equities" | |
AC_CREDIT = "Credit" | |
AC_FIXED_INCOME = "Fixed Income" | |
AC_USD = "USD" | |
AC_COMMODITIES = "Commodities" | |
ESEC_TECH = "Tech" | |
ESEC_FINANCIALS = "Financials" |
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//Replicated from here: https://twitter.com/BuddyCA64525731/status/1246051013557211136 | |
study("BuddyCarterRange", "BCR", overlay=true) | |
atr_lb = input(title="ATR Lookback", defval=60, minval=1, type=input.integer) | |
sma_lb = input(title="SMA Lookback", defval=10, minval=1, type=input.integer) | |
bc_range = atr(atr_lb) * sma(tr(true)/atr(atr_lb), sma_lb)/2 |
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function download_txns(name, txns) { | |
let b = new Uint8Array(0); | |
for(const txn in txns){ | |
b = concatTypedArrays(b, txns[txn]) | |
} | |
var blob = new Blob([b], {type: "application/octet-stream"}); | |
var link = document.createElement('a'); | |
link.href = window.URL.createObjectURL(blob); | |
link.download = name; |
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import numpy as np | |
from scipy import stats | |
import matplotlib.pyplot as plt | |
def compare(n, p): | |
return np.sum([abs(stats.poisson.pmf(x, n*p) - stats.binom.pmf(x, n, p)) for x in range(100)]) | |
max_moneysupply = 1e16 |
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package main | |
import ( | |
"encoding/json" | |
"fmt" | |
"log" | |
"os" | |
"regexp" | |
"strings" |
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