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Notes on Azure functions

Installing the CLI

npm i -g azure-functions-core-tools@core

Creating the Template

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        DispatchQueue.global(qos: .background).async {
            var x:Int = 0
            while (x < Int.max){
                x+=1
                var y:Int = 0
                while (y < Int.max){
                    y+=1
                    if (y % 10000 == 0){
                        DispatchQueue.main.async {
                            self.numbers.text = "value x:\(x) y:\(y)}"

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-- Creates the table
CREATE TABLE geom_points ( 
  id_pk INTEGER PRIMARY KEY
);

-- Adds the columns
SELECT AddGeometryColumn('geom_points','coordinates','4326','POINT',2); -- (long, lat)

-- Inserts the Latitude and Longitude from Geography to Geometry. (long, lat)
INSERT INTO geom_points(id_pk, coordinates)

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// http://www.geodatasource.com/developers/c-sharp

using System;

namespace <your_namespace>
{
    public class GeoCoordinate
    {
        public double Latitude { get; set; }

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        // GET api/values
        [HttpGet, Route("branches")]
        public IActionResult Branches()
        {
            //_context.Regions.Where(u=>u.Business.BusinessId == )

            var regions = _context.Regions
                .Include(x => x.Branches)
                .Include(x => x.Business)
                .ToList();

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/usr/local/mysql/support-files

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Replacement to another file.

cat DATA.TXT | % { $_ -replace "Mary","Susan" } > newfile.txt

Same file.

cat DATA.TXT | % { $_ -replace "Mary","Susan" }

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sudo ln -s "/Applications/Visual Studio Code.app/Contents/MacOS/Electron" /usr/local/bin/code

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#include <Arduino_FreeRTOS.h>
#include <semphr.h>  // add the FreeRTOS functions for Semaphores (or Flags).
#include <LiquidCrystal.h>
#include "pitches.h"  
LiquidCrystal lcd(12, 11, 5, 4, 3, 2);

// Declare a mutex Semaphore Handle which we will use to manage the Serial Port.
// It will be used to ensure only only one Task is accessing this resource at any time.
SemaphoreHandle_t xSerialSemaphore;

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import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the data, converters convert the letter to a number
data= np.loadtxt("D:\OpenCV\opencv\sources\samples\data\letter-recognition.data", dtype= 'float32', delimiter = ',',
                    converters= {0: lambda ch: ord(ch)-ord('A')})

# split the data to two, 10000 each for train and test
train, test = np.vsplit(data,2)