onilton

def sieve(maxNumber: Int) = {
  def innerSieve(p: Int, n: Int, a: Array[Boolean]): Array[Boolean] = 
    if (p >= a.size) a
    else if (!a(p) || n >= a.size) innerSieve(p+1, 2*(p+1), a)
    else { a(n) = false ; innerSieve(p, p + n, a) }
  innerSieve(2, 2, Array.fill(maxNumber+1)(true)).zipWithIndex.filter(_._1).map(_._2)
} 

onilton

implicit class StringArrayImprovements(a: Array[String]) {  def |[T](op: Array[String] => T) = op(a) }
implicit class OptionImprovements(a: Option[String]) {  def |(op: Option[String] => String) = op(a) }



import sys.process._

case class CustomCommand(cmd: Symbol, var args: List[Symbol] = List.empty, var largs: List[Symbol] = List.empty) {
  def -(x: Symbol) = this.copy(args = args :+ x) 
  def --(x: Symbol) = this.copy(largs = largs :+ x) 

onilton

#-----------------------------------------------------------------------------------------
#
# Spectral Matting
#
import time
import logging

import numpy as np
from scipy import ndimage, sparse
import scipy.sparse.linalg as sparse_linalg

onilton

Requirements

• NodeJs (and npm) - https://nodejs.org/en/download/

• vscode - https://code.visualstudio.com/Download

Getting started

• Typescript - https://www.typescriptlang.org/docs/handbook/typescript-in-5-minutes.html

onilton

10 ways os using: https://www.visidata.org/blog/2020/ten/

Cheatsheet : https://jsvine.github.io/visidata-cheat-sheet/en/

Pratice: https://jsvine.github.io/intro-to-visidata/practical/distinctive-birds/

Join sheets: https://jsvine.github.io/intro-to-visidata/intermediate/joining-sheets/

Save and replay session: https://www.visidata.org/docs/save-restore/

onilton

diff --git a/.gitignore b/.gitignore
index e73dd4d9e8..e3192b3a0d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,7 +24,6 @@ out-vscode-reh-web-min/
 out-vscode-reh-web-pkg/
 out-vscode-web/
 out-vscode-web-min/
-src/vs/server
 resources/server