padenot

import pandas as pd
import matplotlib.pyplot as plt
import json
import numpy as np
import sys


# Load data from JSON file
with open(sys.argv[1], 'r') as f:
    data = json.load(f)

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#!/bin/bash

OUTPUT_DIR="compressed_output"
FILES=("model.enet.intgemm.alphas.bin" "model.jaen.intgemm.alphas.bin")
LEVELS=(1 3 9)
ZSTD_LEVELS=(1 5 19)

mkdir -p "$OUTPUT_DIR"

for file in "${FILES[@]}"; do

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+---------------+---------+-------------------+--------+----------------------------------------------------------+
| `sortv`       | 1       | `ts`              | `ts`   | Returns a time series, sorted by value                   |
+---------------+---------+-------------------+--------+----------------------------------------------------------+
| `sortt`       | 1       | `ts`              | `ts`   | Returns a time series, sorted by time                    |
+---------------+---------+-------------------+--------+----------------------------------------------------------+
| `slice`       | 3       | `ts`, `s`, `s`    | `ts`   | Returns subset of time series by index                   |
+---------------+---------+-------------------+--------+----------------------------------------------------------+
| `sliceperc`   | 3       | `ts`, `s`, `s`    | `ts`   | Returns subset of time series by percentage              |
+---------------+---------+-------------------+--------+----------------------------------------

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Install netlify:

npm install -g netlify-cli

Set it up like so, this will open a few web pages and create a netlify account with github login, authorize netlify, setup the github hook for deploy, etc.:

repositories/ringbuf.js::master$ netlify init
Logging into your Netlify account...
Opening https://app.netlify.com/authorize?response_type=ticket&ticket=e527b77c4d5c2a95a57c063ccb193459

padenot

if (window.SIMD) {
  // yay, SIMD available, let's do a little stereo mixdown routine
  for (var i = 0; i < bufsize; i+=4) {
    var lhs = SIMD.float32x4(left[i+0], left[i+1],
                             left[i+2], left[i+3]),
        rhs = SIMD.float32x4(right[i+0], right[i+1],
                             right[i+2], right[i+3]),
        mixdown = SIMD.float32x4();

    mixdown = lhs.add(rhs);

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<script>
function lalaj() {
  var out = document.querySelector("pre");
  var a = new OfflineAudioContext(1, 44100, 44100);
  var b = a.createBuffer(1, 44100, 44100);
  var g = a.createGain();
  var bb = b.getChannelData(0);
  for (var i = 0; i < bb.length; i++) {
    bb[i] = 1.0;
  }

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/* vertical blur of blur_px. for efficiency, we copy a vertical
 * line to a buffer so that access are made in a cache efficient
 * manner */
function vblur(b, blur_px) {
  var buf = new Uint8ClampedArray(h * 4);

  for (var vertical = 0; vertical < w; vertical++) {
    copy_vertical_in_buffer(b, vertical, buf);
    blur_scanline(buf, 0, h, blur_px);

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// Converts a spec made with ReSpec to a cheat sheet
// Shift + f4 in Firefox, copy, paste, enjoy

var idl = document.querySelectorAll(".idl");

var cs = document.createElement("div");
cs.className = "lalala123123"

for (var i of idl) {
  cs.appendChild(i);

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- Web Audio Feature requests: https://github.com/WebAudio/web-audio-api/issues
- Web Audio mailing list: http://lists.w3.org/Archives/Public/public-audio/
- General web feature requests: http://www.whatwg.org/mailing-list
- Other GitHub repos for other whatwg specs, https://github.com/whatwg/
- Twitter to a person you know that implements / specs this kind of stuff, but prefer public venues so you get maximum visibility, and maximize your chances to get an answer

padenot

#!/usr/bin/env python

import matplotlib.pyplot as plt
import pylab as pl
import numpy as np
import os

import sys

data = {}