Monthly Archives: September 2019

What is React js

React Quickstart Tutorial

React is a JavaScript library created by Facebook.
ReactJS is a tool for building UI components.

Adding React to an HTML Page

This quickstart tutorial will add React to a page like this:


<!DOCTYPE html>
<html lang="en">
<title>Test React</title>

<!-- Load React API -->
<!-- Load React DOM-->
<!-- Load Babel Compiler -->


    //  JSX Babel code goes here


What is Babel?

Babel is a JavaScript compiler that can translate markup or programming languages into JavaScript.

With Babel, you can use the newest features of JavaScript (ES6 – ECMAScript 2015).

Babel is available for different conversions. React uses Babel to convert JSX into JavaScript.

Please note that is needed for using Babel.

What is JSX?

JSX stands for JavaScript XML.
JSX is an XML/HTML like extension to JavaScript.


const element = <h1>Hello World!</h1>

As you can see above, JSX is not JavaScript nor HTML.

JSX is a XML syntax extension to JavaScript that also comes with the full power of ES6 (ECMAScript 2015).

Just like HTML, JSX tags can have a tag names, attributes, and children. If an attribute is wrapped in curly braces, the value is a JavaScript expression.

Note that JSX does not use quotes around the HTML text string.

React DOM Render

The method ReactDom.render() is used to render (display) HTML elements:


Hello World!
ReactDOM.render( <h1>Hello React!</h1>, document.getElementById('id01'));

JSX Expressions

Expressions can be used in JSX by wrapping them in curly {} braces.


Hello World!
const name = 'John Doe'; ReactDOM.render( <h1>Hello {name}!</h1>, document.getElementById('id01'));

React Elements

React applications are usually built around a single HTML element.

React developers often call this the root node (root element):

React elements look like this:

const element = <h1>Hello React!</h1>

Elements are rendered (displayed) with the ReactDOM.render() method:

ReactDOM.render(element, document.getElementById('root'));

React elements are immutable. They cannot be changed.

The only way to change a React element is to render a new element every time:


function tick() {
    const element = (<h1>{new Date().toLocaleTimeString()}</h1>);
    ReactDOM.render(element, document.getElementById('root'));
setInterval(tick, 1000);

React Components

React components are JavaScript functions.

This example creates a React component named “Welcome”:


function Welcome() {
    return <h1>Hello React!</h1>;
ReactDOM.render(<Welcome />, document.getElementById('root'));

React can also use ES6 classes to create components.

This example creates a React component named Welcome with a render method:


class Welcome extends React.Component {
    render() { return(<h1>Hello React!</h1>); }
ReactDOM.render(<Welcome />, document.getElementById('root'));

React Component Properties

This example creates a React component named “Welcome” with property arguments:


function Welcome(props) {
    return <h1>Hello {}!</h1>;
ReactDOM.render(<Welcome name="John Doe"/>, document.getElementById('root'));

React can also use ES6 classes to create components.

This example also creates a React component named “Welcome” with property arguments:


class Welcome extends React.Component {
    render() { return(<h1>Hello {}</h1>); }
ReactDOM.render(<Welcome name="John Doe"/>, document.getElementById('root'));

 JSX Compiler

The examples on this page compiles JSX in the browser.

For production code, the compilation should be done separately.

Create React Application

Facebook has created a Create React Application with everything you need to build a React app.

It is a a development server that uses Webpack to compile React, JSX, and ES6, auto-prefix CSS files.

The Create React App uses ESLint to test and warn about mistakes in the code.

To create a Create React App run the following code on your terminal:


npx create-react-app react-tutorial

Make sure you have Node.js 5.2 or higher. Otherwise you must install npx:


npm i npx

Start one folder up from where you want your application to stay:


C:\Users\myUser>npx create-react-app react-tutorial

Success Result:

npx: installed 63 in 10.359s
Creating a new React app in C:\Users\myUser\react-tutorial.
Installing packages. This might take a couple of minutes.
Installing react, react-dom, and react-scripts...
+ react-dom@16.5.2
+ react@16.5.2
+ react-scripts@2.0.4
added 1732 packages from 664 contributors and audited 31900 packages in 355.501s
found 0 vulnerabilities+ react@16.5.2

Success! Created react-tutorial at C:\Users\myUser\react-tutorial
Inside that directory, you can run several commands:

npm start
Starts the development server.

npm run build
Bundles the app into static files for production.

npm test
Starts the test runner.

npm run eject
Removes this tool and copies build dependencies, configuration files
and scripts into the app directory. If you do this, you can't go back!

We suggest that you begin by typing:

cd react-tutorial
npm start
provisioning featured

Apple iOS App Provisioning

A distribution certificate identifies your team/organization within a distribution App Provisioning profile and allows you to submit your app to the Apple App Store.

The workflow for developing and distributing iOS apps can be complex and difficult to understand. This article explain the steps needed to manage certificates and provisioning profiles and assist developer who are starting to develop in-house iOS apps.

A provisioning profile is a collection of digital entities that uniquely ties developers and devices to an authorized iPhone Development Team and enables a device to be used for testing.

The following steps describe the high level activities required to manage and distribute apps.

  1. Manage Certificates for development and production
    1. Create certificate for Development
    2. Create certificate for Production
  2. Register AppId and DeviceId
    1. Add Device ID’s
    2. Register AppID’s
  3. Create App Provisioning Profiles for Project
    1. Create Provisioning Profile for Developement & Production
    2. Apply AppID to Provisioning Profile
    3. Apply Certificate to App Provisioning Profile
    4. Download Profile and add it in Xcode

Step 1: Login

Go to and click on Account (you must have an Apple Developer account to begin)

1. Click Log In, choose Select Certificates, Identifiers & Profiles

Step 2 : create Certificate

On the left menu select Certificates

1. Select add button “+” at the top right to create a new Certificate

2. Select “iOS Distribution (App Store and Ad Hoc)” and press Continue

3. Developers will need to generate a Certificate Signing Request (CSR) from their keychain and perform the Request Certificate function. Then select Signing certificate and Generate and Download certificate.

4. Click on Downloaded Certificate, it will added to KeyChain.

Step 3 : RegisterAppID

  1. From the left menu select Identifiers -> Click on Add new -> Select AppID’s
  2. Describe Name, and paste your project’s BundleID and select Capability which your app provides
  3. Click On Register Button. Now your AppID is Registered.

Step 4 : Create App Provisioning Profile

On the left tab under Provisioning Profiles, select Distribution

1. Select add button “+” at the top right to create a new profile

2. Select “App Store” and press Continue

iOS Provisioning Profile



3. Select App ID and press Continue


iOS Provisioning Profile



4. Select the Certificate you wish to include in this provisioning profile (the certificate the app was signed with) and click Continue. Next, select the devices you wish to include in the provisioning profile. The certificate is a public/private key-pair, which identifies who developed the app.


5. Create a name for your profile and click Generate. You might want to include “Distribution” in the name so you can distinguish this one from testing.

6. Download Your Profile and by clicking it will be added to Xcode.



Howto reverse proxy in nginx

Proxying is typically used to distribute the load among several servers, seamlessly show content from different websites, or pass requests for processing to application servers over protocols other than HTTP.

When NGINX proxies a request, it sends the request to a specified proxied server, fetches the response, and sends it back to the client. It is possible to proxy requests to an HTTP server (another NGINX server or any other server) or a non-HTTP server (which can run an application developed with a specific framework, such as PHP or Python) using a specified protocol.

1. To pass a request to an HTTP proxied server, the proxy_pass directive is specified inside a location. For example:

location /some/path/ {

 2. This address can be specified as a domain name or an IP address. The address may also include a port:

location ~ \.php {

3. To pass a request to a non-HTTP proxied server, the appropriate **_pass directive should be used:

  • fastcgi_pass passes a request to a FastCGI server
  • uwsgi_pass passes a request to a uwsgi server
  • scgi_pass passes a request to an SCGI server
  • memcached_pass passes a request to a memcached server

4. Passing Request Headers

location /some/path/ {
    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_pass http://localhost:8000;


5. To disable buffering in a specific location, place the proxy_buffering directive in the location with the off parameter, as follows:

location /some/path/ {
    proxy_buffering off;
    proxy_pass http://localhost:8000;



Matrix Javascript SDK

Matrix Javascript SDK

Matrix Client-Server r0 SDK for JavaScript. This SDK can be run in a browser or in Node.js.


In a browser

Download either the full or minified version from and add that as a <script> to your page. There will be a global variable matrixcs attached to window through which you can access the SDK. See below for how to include libolm to enable end-to-end-encryption.

Please check the working browser example for more information.

In Node.js

Ensure you have the latest LTS version of Node.js installed.

Using yarn instead of npm is recommended. Please see the Yarn install guide if you do not have it already.

yarn add matrix-js-sdk

var sdk = require("matrix-js-sdk");
var client = sdk.createClient("");
client.publicRooms(function(err, data) {
    console.log("Public Rooms: %s", JSON.stringify(data));

See below for how to include libolm to enable end-to-end-encryption. Please check the Node.js terminal app for a more complex example.

To start the client:

await client.startClient({initialSyncLimit: 10});

You can perform a call to /sync to get the current state of the client:

client.once('sync', function(state, prevState, res) {
    if(state === 'PREPARED') {
    } else {

To send a message:

var content = {
    "body": "message text",
    "msgtype": "m.text"
client.sendEvent("roomId", "", content, "", (err, res) => {

To listen for message events:

client.on("Room.timeline", function(event, room, toStartOfTimeline) {
  if (event.getType() !== "") {
    return; // only use messages

By default, the matrix-js-sdk client uses the MemoryStore to store events as they are received. For example to iterate through the currently stored timeline for a room:

Object.keys( => {
  client.getRoom(roomId).timeline.forEach(t => {

What does this SDK do?

This SDK provides a full object model around the Matrix Client-Server API and emits events for incoming data and state changes. Aside from wrapping the HTTP API, it:

  • Handles syncing (via /initialSync and /events)
  • Handles the generation of “friendly” room and member names.
  • Handles historical RoomMember information (e.g. display names).
  • Manages room member state across multiple events (e.g. it handles typing, power levels and membership changes).
  • Exposes high-level objects like Rooms, RoomState, RoomMembers and Users which can be listened to for things like name changes, new messages, membership changes, presence changes, and more.
  • Handle “local echo” of messages sent using the SDK. This means that messages that have just been sent will appear in the timeline as ‘sending’, until it completes. This is beneficial because it prevents there being a gap between hitting the send button and having the “remote echo” arrive.
  • Mark messages which failed to send as not sent.
  • Automatically retry requests to send messages due to network errors.
  • Automatically retry requests to send messages due to rate limiting errors.
  • Handle queueing of messages.
  • Handles pagination.
  • Handle assigning push actions for events.
  • Handles room initial sync on accepting invites.
  • Handles WebRTC calling.

Later versions of the SDK will:

  • Expose a RoomSummary which would be suitable for a recents page.
  • Provide different pluggable storage layers (e.g. local storage, database-backed)



 Emitted events

The SDK will emit events using an EventEmitter. It also emits object models (e.g. Rooms, RoomMembers) when they are updated.

// Listen for low-level MatrixEvents
client.on("event", function(event) {

// Listen for typing changes
client.on("RoomMember.typing", function(event, member) {
    if (member.typing) {
      console.log( + " is typing...");
    else {
      console.log( + " stopped typing.");

// start the client to setup the connection to the server

Promises and Callbacks

Most of the methods in the SDK are asynchronous: they do not directly return a result, but instead return a Promise which will be fulfilled in the future.

The typical usage is something like:

matrixClient.someMethod(arg1, arg2).done(function(result) {

Alternatively, if you have a Node.js-style callback(err, result) function, you can pass the result of the promise into it with something like:

matrixClient.someMethod(arg1, arg2).nodeify(callback);

The main thing to note is that it is an error to discard the result of a promise-returning function, as that will cause exceptions to go unobserved. If you have nothing better to do with the result, just call .done() on it. See for more information.

Methods which return a promise show this in their documentation.

Many methods in the SDK support both Node.js-style callbacks and Promises, via an optional callback argument. The callback support is now deprecated: new methods do not include a callback argument, and in the future it may be removed from existing methods.


This section provides some useful code snippets which demonstrate the core functionality of the SDK. These examples assume the SDK is setup like this:

var sdk = require("matrix-js-sdk");
var myUserId = "@example:localhost";
var myAccessToken = "QGV4YW1wbGU6bG9jYWxob3N0.qPEvLuYfNBjxikiCjP";
var matrixClient = sdk.createClient({
    baseUrl: "http://localhost:8008",
    accessToken: myAccessToken,
    userId: myUserId

Automatically join rooms when invited

matrixClient.on("RoomMember.membership", function(event, member) {
    if (member.membership === "invite" && member.userId === myUserId) {
        matrixClient.joinRoom(member.roomId).done(function() {
           console.log("Auto-joined %s", member.roomId);


Print out messages for all rooms

matrixClient.on("Room.timeline", function(event, room, toStartOfTimeline) {
    if (toStartOfTimeline) {
        return; // don't print paginated results
    if (event.getType() !== "") {
        return; // only print messages
        // the room name will update with events automatically
        "(%s) %s :: %s",, event.getSender(), event.getContent().body



(My Room) @megan:localhost :: Hello world
(My Room) @megan:localhost :: how are you?
(My Room) @example:localhost :: I am good
(My Room) @example:localhost :: change the room name
(My New Room) @megan:localhost :: done

Print out membership lists whenever they are changed

matrixClient.on("RoomState.members", function(event, state, member) {
    var room = matrixClient.getRoom(state.roomId);
    if (!room) {
    var memberList = state.getMembers();
    console.log(Array( + 1).join("="));  // underline
    for (var i = 0; i < memberList.length; i++) {
       "(%s) %s",



My Room
(join) @example:localhost
(leave) @alice:localhost
(join) Bob
(invite) @charlie:localhost

API Reference

A hosted reference can be found at

This SDK uses JSDoc3 style comments. You can manually build and host the API reference from the source files like this:

$ yarn gendoc
$ cd .jsdoc
$ python -m SimpleHTTPServer 8005

Then visit http://localhost:8005 to see the API docs.

End-to-end encryption support

The SDK supports end-to-end encryption via the Olm and Megolm protocols, using libolm. It is left up to the application to make libolm available, via the Olm global.

It is also necessry to call matrixClient.initCrypto() after creating a new MatrixClient (but before calling matrixClient.startClient()) to initialise the crypto layer.

If the Olm global is not available, the SDK will show a warning, as shown below; initCrypto() will also fail.

Unable to load crypto module: crypto will be disabled: Error: global.Olm is not defined

If the crypto layer is not (successfully) initialised, the SDK will continue to work for unencrypted rooms, but it will not support the E2E parts of the Matrix specification.

To provide the Olm library in a browser application:

To provide the Olm library in a node.js application:

  • yarn add (replace the URL with the latest version you want to use from
  • global.Olm = require('olm'); before loading matrix-js-sdk.

If you want to package Olm as dependency for your node.js application, you can use yarn add If your application also works without e2e crypto enabled, add --optional to mark it as an optional dependency.


This section is for people who want to modify the SDK. If you just want to use this SDK, skip this section.

First, you need to pull in the right build tools:

$ yarn install


To build a browser version from scratch when developing::

$ yarn build

To constantly do builds when files are modified (using watchify)::

$ yarn watch

To run tests (Jasmine)::

$ yarn test

To run linting:

$ yarn lint

WSL vs WSL 2 – performance

WSL 2 is a new version of the architecture that powers the Windows Subsystem for Linux to run ELF64 Linux binaries on Windows. Its primary goals are to increase file system performance, as well as adding full system call compatibility. This new architecture changes how these Linux binaries interact with Windows and your computer’s hardware, but still provides the same user experience as in WSL 1 (the current widely available version). Individual Linux distros can be run either as a WSL 1 distro, or as a WSL 2 distro, can be upgraded or downgraded at any time, and you can run WSL 1 and WSL 2 distros side by side. WSL 2 uses an entirely new architecture that uses a real Linux kernel.

It’s a major reworking of the original WSL concept, moving away from translating Linux system calls to Windows to shipping a complete Linux kernel that runs alongside Windows’ own kernel.

The reasons for doing this are many, but the main one is simple: It’s impossible for an emulator that ships twice a year to keep up with the changes in the Linux kernel, changes that Linux binaries depend on. If Windows is to support developers building Linux apps for the cloud, then it needs to be more than consistent, it needs to be compatible.


Linux kernel in WSL 2

The Linux kernel in WSL 2 is built in house from the latest stable branch, based on the source available at This kernel has been specially tuned for WSL 2. It has been optimized for size and performance to give an amazing Linux experience on Windows and will be serviced through Windows updates, which means you will get the latest security fixes and kernel improvements without needing to manage it yourself.

Increased file IO performance

File intensive operations like git clone, npm install, apt update, apt upgrade, and more will all be noticeably faster. The actual speed increase will depend on which app you’re running and how it is interacting with the file system. Initial versions of WSL 2 run up to 20x faster compared to WSL 1 when unpacking a zipped tarball, and around 2-5x faster when using git clone, npm install and cmake on various projects.

Sockets performance benchmarks






The Ubuntu 18.04 LTS WSL instance was used for testing with its default packages. In addition to looking at the WSL1 vs. WSL2 performance of Ubuntu 18.04, Ubuntu 18.04.2 LTS itself was also tested bare metal on the same system for looking at the raw performance of Ubuntu on the Intel desktop being tested.

Full System Call Compatibility

Linux binaries use system calls to perform many functions such as accessing files, requesting memory, creating processes, and more. In WSL 1 we created a translation layer that interprets many of these system calls and allows them to work on the Windows NT kernel. However, it’s challenging to implement all of these system calls, resulting in some apps being unable to run in WSL 1. Now that WSL 2 includes its own Linux kernel it has full system call compatibility. This introduces a whole new set of apps that you can run inside of WSL. Some exciting examples are the Linux version of Docker, as well as FUSE!

Using WSL 2 means you can also get the most recent improvements to the Linux kernel much faster than in WSL 1, as we can simply update the WSL 2 kernel rather than needing to reimplement the changes ourselves.

WSL 2 will be a much more powerful platform for you to run your Linux apps on and will empower you to do more with a Linux environment on Windows.



Openvas installation in CentOS 7

What is Openvas?

OpenVAS (Open Vulnerability Assessment System, originally known as GNessUs) is a software framework of several services and tools offering vulnerability scanning and vulnerability management.

All OpenVAS products are free software, and most components are licensed under the GNU General Public License (GPL). Plugins for OpenVAS are written in the Nessus Attack Scripting Language, NASL.

The primary reason to use this scan type is to perform comprehensive security testing of an IP address. It will initially perform a port scan of an IP address to find open services. Once listening services are discovered they are then tested for known vulnerabilities and misconfiguration using a large database (more than 53000 NVT checks). The results are then compiled into a report with detailed information regarding each vulnerability and notable issues discovered.

Once you receive the results of the tests, you will need to check each finding for relevance and possibly false positives. Any confirmed vulnerabilities should be re-mediated to ensure your systems are not at risk.

Vulnerability scans performed from externally hosted servers give you the same perspective as an attacker. This has the advantage of understanding exactly what is exposed on external-facing services.

Step 1: Disable SELinux


sed -i ‘s/=enforcing/=disabled/’ /etc/selinux/config


and reboot the machine.

Step 2:  Install dependencies


yum -y install wget rsync curl net-tools


Step 3: Install OpenVAS repository

install the official repository so that OpenVAS works appropriately in the analysis of vulnerabilities.


wget -q -O – |sh


Step 4: Install OpenVAS


yum -y install openvas


Step 5: Run OpenVAS

Once OpenVAS is installed, we continue to start it by executing the following command:




Once downloaded it will be necessary to configure the GSAD IP address, Greenbone Security Assistant, which is a web interface to manage system scans.

Step 6: Configure OpenVAS Connectivity

We go to our browser and enter the IP address of the CentOS 7 server where we have installed OpenVAS, and we will see that the following message is displayed:

Openvas dashboard


Automatic NVT Updates With Cron


35 1 * * * /usr/sbin/greenbone-nvt-sync > /dev/null
5 0 * * * /usr/sbin/greenbone-scapdata-sync > /dev/null
5 1 * * * /usr/sbin/greenbone-certdata-sync > /dev/null



Python Matplotlib Library with Examples

What Is Python Matplotlib?

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+.

Pyplot is a Matplotlib module which provides a MATLAB-like interface. Matplotlib is designed to be as usable as MATLAB, with the ability to use Python and the advantage of being free and open-source. matplotlib.pyplot is a plotting library used for 2D graphics in the python programming language. It can be used in python scripts, shell, web application servers, and other graphical user interface toolkits.

There are several toolkits that are available that extend python Matplotlib functionality.

  • Basemap: It is a map plotting toolkit with various map projections, coastlines, and political boundaries.
  • Cartopy: It is a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.
  • Excel tools: Matplotlib provides utilities for exchanging data with Microsoft Excel.
    Mplot3d: It is used for 3-D plots.
  • Natgrid: It is an interface to the “natgrid” library for irregular gridding of the spaced data.
  • GTK tools: mpl_toolkits.gtktools provides some utilities for working with GTK. This toolkit ships with matplotlib, but requires pygtk.
  • Qt interface
  • Mplot3d: The mplot3d toolkit adds simple 3D plotting capabilities to matplotlib by supplying an axes object that can create a 2D projection of a 3D scene.
  • matplotlib2tikz: export to Pgfplots for smooth integration into LaTeX documents.

Types of Plots
There are various plots which can be created using python Matplotlib. Some of them are listed below:

  • Bar Graph
  • Histogram
  • Scatter Plot
  • Line Plot
  • 3D plot
  • Area Plot
  • Pie Plot
  • Image Plot

We will demonstrate some of them in detail.

But before that, let me show you elementary codes in python matplotlib in order to generate a simple graph.

from matplotlib import pyplot as plt
#Plotting to our canvas
#Showing what we plotted

So, with three lines of code, you can generate a basic graph using python matplotlib.

Let us see how can we add title, labels to our graph created by python matplotlib library to bring in more meaning to it. Consider the below example:

from matplotlib import pyplot as plt
plt.ylabel('Y axis')
plt.xlabel('X axis')

You can even try many styling techniques to create a better graph by changing the width or color of a particular line or what if you want to have some grid lines, there you need styling!

The style package adds support for easy-to-switch plotting “styles” with the same parameters as a matplotlibrc file.

There are a number of pre-defined styles provided by matplotlib. For example, there’s a pre-defined style called “ggplot”, which emulates the aesthetics of ggplot (a popular plotting package for R). To use this style, just add:

import matplotlib.pyplot as plt'ggplot')

To list all available styles, use:

['seaborn-darkgrid', 'Solarize_Light2', 'seaborn-notebook', 'classic', 'seaborn-ticks', 'grayscale', 'bmh', 'seaborn-talk', 'dark_background', '
ggplot', 'fivethirtyeight', '_classic_test', 'seaborn-colorblind', 'seaborn-deep', 'seaborn-whitegrid', 'seaborn-bright', 'seaborn-poster', 'sea
born-muted', 'seaborn-paper', 'seaborn-white', 'fast', 'seaborn-pastel', 'seaborn-dark', 'tableau-colorblind10', 'seaborn', 'seaborn-dark-palett

So, let me show you how to add style to a graph using python matplotlib. First, you need to import the style package from python matplotlib library and then use styling functions as shown in below code:

from matplotlib import pyplot as plt
from matplotlib import style
x = [5,8,10]
y = [12,16,6]
x2 = [6,9,11]
y2 = [6,15,7]
plt.plot(x,y,'r-o',label='line one', linewidth=5)
plt.plot(x2,y2,'c',label='line two',linewidth=5)
plt.title('Epic Info')
plt.ylabel('Y axis')
plt.xlabel('X axis')

import numpy as np
import matplotlib.pyplot as plt
  plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')
# Some plotting code with the default style

Now, we will understand the different kinds of plots. Let’s start with the bar graph!

Matplotlib: Bar Graph
A bar graph uses bars to compare data among different categories. It is well suited when you want to measure the changes over a period of time. It can be plotted vertically or horizontally. Also, the vital thing to keep in mind is that longer the bar, the greater is the value. Now, let us practically implement it using python matplotlib.

from matplotlib import pyplot as plt
label="BMW", color='b', width=0.2)[1.2,2.2,3.2,4.2,5.2],[2,3,5,10,8],
label="Audi", color='r',width=0.2)
plt.ylabel('Distance (kms)')
plt.title('Bar Plot')

When I run this code, it generates a figure like below:

In the above plot, I have displayed a comparison between the distance covered by two cars BMW and Audi over a period of 5 days. Next, let us move on to another kind of plot using python matplotlib – Histogram

Matplotlib – Histogram
Let me first tell you the difference between a bar graph and a histogram. Histograms are used to show a graphical representation of the distribution of numerical data whereas a bar chart is used to compare different entities.

It is an estimate of the probability distribution of a continuous variable (quantitative variable) and was first introduced by Karl Pearson. It is a kind of bar graph.

To construct a histogram, the first step is to “bin” the range of values — that is, divide the entire range of values into a series of intervals — and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent and are often (but are not required to be) of equal size.

Basically, histograms are used to represent data given in the form of some groups or we can say when you have arrays or a very long list. X-axis is about bin ranges where Y-axis talks about frequency. So, if you want to represent the age-wise population in form of the graph then histogram suits well as it tells you how many exist in certain group range or bin if you talk in the context of histograms.

In the below code, I have created the bins in the interval of 10 which means the first bin contains elements from 0 to 9, then 10 to 19 and so on.

import matplotlib.pyplot as plt
population_age = [22,55,62,45,21,22,34,42,42,4,2,102,95,85,55,110,120,70,65,55,111,115,80,75,65,54,44,43,42,48]
bins = [0,10,20,30,40,50,60,70,80,90,100]
plt.hist(population_age, bins, rwidth=0.8)
plt.xlabel('age groups')
plt.ylabel('Number of people')

When I run this code, it generates a figure like below:

As you can see in the above plot, Y-axis tells about the age groups that appear with respect to the bins. Our biggest age group is between 40 and 50.

Matplotlib: Scatter Plot
A scatter plot is a type of plot that shows the data as a collection of points. The position of a point depends on its two-dimensional value, where each value is a position on either the horizontal or vertical dimension. Usually, we need scatter plots in order to compare variables, for example, how much one variable is affected by another variable to build a relation out of it.
Consider the below example:

import matplotlib.pyplot as plt
x = [1,1.5,2,2.5,3,3.5,3.6]
y = [7.5,8,8.5,9,9.5,10,10.5]
plt.scatter(x,y, label='high income low saving',color='r')
plt.scatter(x1,y1,label='low income high savings',color='b')
plt.title('Scatter Plot')

As you can see in the above graph, I have plotted two scatter plots based on the inputs specified in the above code. The data is displayed as a collection of points having ‘high-income low salary’ and ‘low-income high salary.’

Scatter plot with groups
Data can be classified into several groups. The code below demonstrates:

import numpy as np
import matplotlib.pyplot as plt

# Create data
N = 60
g1 = (0.6 + 0.6 * np.random.rand(N), np.random.rand(N))
g2 = (0.4+0.3 * np.random.rand(N), 0.5*np.random.rand(N))
g3 = (0.3*np.random.rand(N),0.3*np.random.rand(N))

data = (g1, g2, g3)
colors = ("red", "green", "blue")
groups = ("coffee", "tea", "water")

# Create plot
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(1, 1, 1)

for data, color, group in zip(data, colors, groups):
   x, y = data
   ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=30, label=group)

plt.title('Matplot scatter plot')

The purpose of using “plt.figure()” is to create a figure object. It’s a Top-level container for all plot elements.

The whole figure is regarded as the figure object. It is necessary to explicitly use “plt.figure()”when we want to tweak the size of the figure and when we want to add multiple Axes objects in a single figure.

fig.add_subplot() is used to control the default spacing of the subplots.
For example, “111” means “1×1 grid, first subplot” and “234” means “2×3 grid, 4th subplot”.

You can easily understand by the following picture:

Next, let us understand the area plot or you can also say Stack plot using python matplotlib.

Matplotlib: Area Plot
Area plots are pretty much similar to the line plot. They are also known as stack plots. These plots can be used to display the evolution of the value of several groups on the same graphic. The values of each group are displayed on top of each other. It allows checking on the same figure the evolution of both the total of a numeric variable and the importance of each group.

A line chart forms the basis of an area plot, where the region between the axis and the line is represented by colors.

import numpy as np
import matplotlib.pyplot as plt

# Your x and y axis
y=[ [1,4,6,8,9], [2,2,7,10,12], [2,8,5,10,6] ]# Basic stacked area chart.
plt.stackplot(x,y, labels=['A','B','C'], colors=['m','c','r'])
plt.legend(loc='upper left')

import matplotlib.pyplot as plt
days = [1,2,3,4,5]
Enfield =[50,40,70,80,20]
Honda = [80,20,20,50,60]
Yahama =[70,20,60,40,60]
KTM = [80,20,20,50,60]
plt.stackplot(days, Enfield, Honda, Yahama, KTM,labels=['Enfield', 'Honda', 'Yahama', 'KTM'], colors=['r','c','y','m'])
plt.ylabel('Distance in kms')
plt.title('Bikes deatils in area plot')

The above-represented graph shows how an area plot can be plotted for the present scenario. Each shaded area in the graph shows a particular bike with the frequency variations denoting the distance covered by the bike on different days. Next, let us move to our last yet most frequently used plot – Pie chart.

Matplotlib: Pie Chart
In a pie plot, statistical data can be represented in a circular graph where the circle is divided into portions i.e. slices of pie that denote a particular data, that is, each portion is proportional to different values in the data. This sort of plot can be mainly used in mass media and business.

import matplotlib.pyplot as plt
slices = [8,5,5,6]
activities = ['Enfield','Honda','Yahama','KTM']
cols = ['c','g','y','b']
shadow= True,
plt.title('Bike details in Pie Plot')

In the above-represented pie plot, the bikes scenario is illustrated, and I have divided the circle into 4 sectors, each slice represents a particular bike and the percentage of distance traveled by it. Now, if you have noticed these slices adds up to 24 hrs, but the calculation of pie slices is done automatically for you. In this way, the pie charts are really useful as you don’t have to be the one who calculates the percentage of the slice of the pie.

Matplotlib: 3D Plot
Plotting of data along x, y, and z axes to enhance the display of data represents the 3-dimensional plotting. 3D plotting is an advanced plotting technique that gives us a better view of the data representation along the three axes of the graph.

Line Plot 3D

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [1,2,3,4,5]
y = [50,40,70,80,20]
y2 = [80,20,20,50,60]
y3 = [70,20,60,40,60]
y4 = [80,20,20,50,60]
plt.plot(x,y,'g',label='Enfield', linewidth=5)
plt.title('bike details in line plot')
plt.ylabel('Distance in kms')

In the above-represented 3D graph, a line graph is illustrated in a 3-dimensional manner. We make use of a special library to plot 3D graphs which are given in the following syntax.
Syntax for plotting 3D graphs:

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection=’3d’)

The import Axes3D is mainly used to create an axis by making use of the projection=3d keyword. This enables a 3-dimensional view of any data that can be written along with the above-mentioned code.

Surface Plot 3D

Axes3D.plot_surface(X, Y, Z, *args, **kwargs)

By default, it will be colored in shades of a solid color, but it also supports color mapping by supplying the cmap argument.

The rstride and cstride kwargs set the stride used to sample the input data to generate the graph. If 1k by 1k arrays are passed in, the default values for the strides will result in a 100×100 grid being plotted. Defaults to 10. Raises a ValueError if both stride and count kwargs (see next section) are provided.

from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm)

Matplotlib: Image Plot

from pylab import *
from numpy import NaN

xmin, xmax, ymin, ymax = -2, 0.8, -1.5, 1.5
max_it = 100    # maximum number of iterations
px     = 3000	# vertical lines
res    = (ymax - ymin) / px   # grid resolution

figure(figsize = (10, 10))

def m(c):
	z = 0
	for n in range(1, max_it + 1):
		z = z**2 + c
		if abs(z) > 2:
			return n
	return NaN

X = arange(xmin, xmax + res, res)
Y = arange(ymin, ymax + res, res)
Z = zeros((len(Y), len(X)))

for iy, y in enumerate(Y):
	print (iy + 1, "of", len(Y))
	for ix, x in enumerate(X):
		Z[-iy - 1, ix] = m(x + 1j * y)

save("mandel", Z)	# save array to file

imshow(Z, cmap =, interpolation = 'none',
  extent = (X.min(), X.max(), Y.min(), Y.max()))

Matplotlib: Working With Multiple Plots
I have discussed multiple types of plots in python matplotlib such as bar plot, scatter plot, pie plot, area plot, etc. Now, let me show you how to handle multiple plots.

import numpy as np
import matplotlib.pyplot as plt
def f(t):
    return np.exp(-t) * np.cos(2*np.pi*t)
t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
t3 = np.arange(0.0, 5.0, 0.02)

plt.plot(t1, f(t1), 'bo', t2, f(t2))
plt.plot(t2, np.cos(2*np.pi*t2))
plt.plot(t3, np.tan(2*np.pi*t2))

How to create letsencrypt wildcard certificates

What’s Certbot?

Certbot is a free, open-source software tool for automatically using Let’s Encrypt certificates on manually-administrated websites to enable HTTPS.

Wildcard certificates

Let’s Encrypt supports wildcard certificate via ACMEv2 using the DNS-01 challenge.

It is necessary to add a TXT record specified by Certbot to the DNS server.

Caution: As it is necessary to update Let’s Encrypt’s certificate every 90 days, a new TXT record is required at every renewal.


Step 1: Run command

certbot certonly --manual --preferred-challenges dns --server --manual-public-ip-logging-ok -d '*.<your.domain>' -d <your.domain>


Step 2: Update DNS TXT record

Saving debug log to /var/log/letsencrypt/letsencrypt.log
Plugins selected: Authenticator manual, Installer None
Obtaining a new certificate
Performing the following challenges:
dns-01 challenge for <your.domain>

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Please deploy a DNS TXT record under the name
_acme-challenge.<your.domain> with the following value:


Before continuing, verify the record is deployed.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Press Enter to Continue


After a successful verification

Waiting for verification...
Cleaning up challenges

 - Congratulations! Your certificate and chain have been saved at:
   Your key file has been saved at:
   Your cert will expire on 2019-09-03. To obtain a new or tweaked
   version of this certificate in the future, simply run certbot
   again. To non-interactively renew *all* of your certificates, run
   "certbot renew"
 - If you like Certbot, please consider supporting our work by:

   Donating to ISRG / Let's Encrypt:
   Donating to EFF: