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-- settings
-- key_binding: press the key specified below
--      to cycle between denoise filters below,
--      set it to nil to disable the binding
local key_binding = "n"
-- key_binding_reverse cycle between denoise filters below
--      in reverse order, set it to nil to disable the binding,
--      set it to a single-char string to enable
local key_binding_reverse = nil

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If you are on a Mac, substitute command for control. Don't type the + (it means press both keys at once).

Shortcuts when in either command mode (outside the cells) or edit mode (inside a cell):

• Shift + Enter run selected cell or cells - if no cells below, insert a code cell below

• Ctrl + B toggle hide/show left sidebar

• Ctrl + S save and checkpoint

• Ctrl + Shift + S save as

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Salesforce OAuth 2.0 JWT Bearer Token Flow Walk-Through

This document will walk you through how to create or configure a Salesforce application for use with JWT authentication. These configuration steps and the example code works as of Salesforce API version 42.0.

Prerequisites

Create an RSA x509 private key/certification pair

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Windows Registry Editor Version 5.00

[HKEY_CLASSES_ROOT\Directory\Background\shell\WSL2]
@="Open Ubuntu Bash here"
"Icon"="Path\\to\\your\\file.ico"

[HKEY_CLASSES_ROOT\Directory\Background\shell\WSL2\command]
@="C:\Windows\System32\wsl.exe"

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28

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ashu

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@"%SystemRoot%\System32\WindowsPowerShell\v1.0\powershell.exe" -NoProfile -InputFormat None -ExecutionPolicy Bypass -Command "iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))" && SET "PATH=%PATH%;%ALLUSERSPROFILE%\chocolatey\bin"

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#!/usr/bin/env python

# This script will pixelate most jpg and png images
# It will both show you the result and save it

import sys

import matplotlib.pyplot as plt
import numpy as np
import PIL.Image as Image

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from PIL import Image

backgroundColor = (0,)*3
pixelSize = 9

image = Image.open('input.png')
image = image.resize((image.size[0]/pixelSize, image.size[1]/pixelSize), Image.NEAREST)
image = image.resize((image.size[0]*pixelSize, image.size[1]*pixelSize), Image.NEAREST)
pixel = image.load()

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py -m pip install opencv-python numpy scipy pandas matplotlib