ndamulelonemakh

#! /bin/bash

# Pre-requisits
# Active Azure Subscription
# A virtual network resource
# 2 or more vms serving the same web app
# Reference: https://docs.microsoft.com/en-us/learn/modules/load-balance-web-traffic-with-application-gateway/5-exercise-create-configure-application-gateway

# i.Step 1: Create a gateway subnet

ndamulelonemakh

#! bin/bash
# Helper script to deploy the production optimised build of a react app to Azure

# The environment variables below can be changed as required
ResourceGroupName="my-example-rg"
Location="westus"      
StorageAccountName="myspecialstorageaccount"
StorageSKU="Standard_LRS"              
StorageKind="StorageV2"   # Allowed values: BlobStorage, BlockBlobStorage, FileStorage, Storage, StorageV2
StorageContainerName="mysitecontainer01"

ndamulelonemakh

# i. Search for tweets containing hastag '#messi' posted in 2020
# Results will be saved to a csv file named 'messitweets.csv'
# Other filters: Only get tweets in english(en), stats: show the number of likes, retweets for each post, count: show the number of posts returned
twint --search "#messi" --since 2020-01-01 --until 2020-12-31 --limit 50000 --popular --csv -o "messitweets.csv" --lang en --stats --hashtags --stats --count


# ii. Collect all tweets from a users timeline
twint --since 2020-01-01 --until 2020-12-31 --limit 50000 --popular --csv -o "out.csv" --lang en --stats --hashtags --stats --count -u endeesa -tl

ndamulelonemakh

#!/bin/bash
# Summary: Install R in Ubuntu and make it available in jupyter lab

# Install R on ubuntu (Tested on Ubuntu 18.04)
sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/'
sudo apt update -y
sudo apt-get install r-base r-base-core r-recommended


# Extra requirements for compiling packages locally

ndamulelonemakh

# Method 1: Easiest and does not require any library
data(mtcars)

## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)

ndamulelonemakh

'''
Simple method to dynamically create the google service account file in Azure App service

Steps
----------------------
i.   Add the service account file contents to Azure App Service configuration
ii.  The json contents will be available to your app at runtime as an environment variable, e.g. GOOGLE_CREDENTIALS
iii. When you app starts, save the contents of the environment variable to a file that you specified as your GOOGLE_APPLICATION_CREDENTIALS
'''
import os

ndamulelonemakh

"""Example implementation of the conjugate gradient descent algorithm using a hard coded objective function

    F(X1, X2) = 5X1**2 + X2**2 + 4X1X2 - 14X1 - 6X2 + 20

- At each step the value of X will be updated using
    X_k+1 = X_k + alpha * Pk

    Where pk is the conjugate direction and alpha is the step length
"""
import math

ndamulelonemakh

public class SimpleSingleton{
  private int count = 1;

  // Use lazy<T> to delay initialisation until the field is accessed
  private static readonly Lazy<SimpleSingleton> _instance = new Lazy<SimpleSingleton>( ()=> new SimpleSingleton());

  private SimpleSingleton(){
    Console.WriteLine($"{nameof(SimpleSingleton)} constructor called at {DateTime.Now}");
  }

ndamulelonemakh

import os
import uuid
import webbrowser
import urllib.parse as urllib

# You need to install these modules using pip
import flask
import requests

__author__ = '@NdamuleloNemakh'

ndamulelonemakh

import smtplib
import ssl
from smtplib import SMTPAuthenticationError

# Simple Python snippet to automatically send emails using gmail
# Docs: https://docs.python.org/3.10/library/smtplib.html

DEFAULT_SSL_PORT = 465
GMAIL_SERVER_ADDRESS = "smtp.gmail.com"
DEFAULT_SENDER_EMAIL_ADDRESS = "<sender-address>"