How to create randomly generated backgrounds with the CSS Paint API

The CSS Paint API (aka CSS Custom Paint) enables developers to write JavaScript functions to draw images into CSS properties such as background-image, border-image, etc.

• Introduction – 00:00
• Getting started with the CSS Paint API – 02:01
• Add the CSS paint() function – 02:50
• Write an external paint worklet file – 03:20
• Dynamic backgrounds with the CSS Paint API – 06:50
• Creating randomly generated backgrounds – 09:20

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The CSS Paint API (aka CSS Custom Paint) enables developers to write JavaScript functions to draw images into CSS properties such as background-image, border-image, etc. In this article, we’ll discuss the basics of the CSS Paint API and, specifically, how to create randomly generated backgrounds.

What is the CSS Paint API

The CSS Paint API is part of CSS Houdini, a set of low-level APIs that give developers direct access to the CSS Object Model (CSSOM). With Houdini, developers can create their own CSS features, even they are not implemented in the browsers.

Typically, we would add a background image to an element like this:

body {
  background-image: url('path/to/image.jpg');
}

This image is static. If you think technically, when the browser parses this CSS code, it sends an HTTP request to the URL and fetches the image. It then displays the image as the background image of body.

Unlike static images, you can use the CSS Paint API to create dynamic backgrounds. Keep reading to see how.

Getting started with the CSS Paint API

To begin using the CSS Paint API, start with the following steps.

1. Add the CSS paint() function
2. Write an external paint worklet file
3. Invoke the worklet in the main thread

Before creating a dynamic background, let’s start with a simple static background composed of bubbles.

First, we need to establish an element to style. We’ll use a simple <div> element.

<!-- index.html -->
<div id="bubble-background"></div>

Step 1: Add the CSS paint() function

To use CSS Paint API for the background, add the paint() function to the background-image property of an element.

div#bubble-background {
  width:400px;
  height:200px;
  background-image: paint(bubblePaint);
}

bubblePaint is the worklet we’ll create in the next steps.

Step 2: Write an external paint worklet file

We need to keep the worklets in an external JavaScript file — we’ll call it bubble-paint.js.

// bubble-paint.js
registerPaint('bubblePaint', class {
  paint(ctx, geom) {
    const circleSize = 10; 
    const bodyWidth = geom.width;
    const bodyHeight = geom.height;

    const maxX = Math.floor(bodyWidth / circleSize);
    const maxY = Math.floor(bodyHeight / circleSize); 

    for (let y = 0; y < maxY; y++) {
      for (let x = 0; x < maxX; x++) {
        ctx.fillStyle = '#eee';
        ctx.beginPath();
        ctx.arc(x * circleSize * 2 + circleSize, y * circleSize * 2 + circleSize, circleSize, 0, 2 * Math.PI, true);
        ctx.closePath();
        ctx.fill();
      }
    }
  }
});

In this file, the registerPaint() function registers a paint worklet. The first parameter is the name of the worklet (same as the one we used in paint(bubblePaint)). The next parameter should be a class with the paint() method.

The paint() method is where we write the JavaScript code to render the image. Here we’ve used two arguments:

1. ctx is similar to CanvasRenderingContext2D (the return value of canvas.getContext("2d")), though not identical. According to Google:

   A paint worklet’s context is not 100% the same as a <canvas> context. As of now, text rendering methods are missing and for security reasons you cannot read back pixels from the canvas.

2. geom contains two elements: the width and height of the painting element

Inside the function, there is some logic to create the pattern. The ctx. functions are what we use to create canvases. If you are not familiar with canvases, I’d suggest you to go through this Canvas API tutorial.

Step 3: Invoke the worklet in the main thread

The next step is to invoke the worklet in the main JavaScript thread (usually in the HTML file).

Dynamic backgrounds with the CSS Paint API

Let’s make the color and size of the above bubbles dynamic. It’s pretty simple with CSS variables.

Step 1: Add CSS variables

div#bubble-background {
  --bubble-size: 40;
  --bubble-color: #eee;

  // other styles
}

Step 2: Use CSS variables

To use those CSS variables in the paint() method, we must first tell the browser we’re going to use it. This is done by adding the inputProperties() static attribute to the class.

// bubble-paint.js
registerPaint('bubblePaint', class {
  static get inputProperties() { return ['--bubble-size', '--bubble-color']; }

  paint() { /* */ }
});

We can access those properties from the third parameter of the paint() function.

paint(ctx, geom, properties) {
    const circleSize = parseInt(properties.get('--bubble-size').toString());
    const circleColor = properties.get('--bubble-color').toString(); 

    const bodyWidth = geom.width;
    const bodyHeight = geom.height;

    const maxX = Math.floor(bodyWidth / circleSize);
    const maxY = Math.floor(bodyHeight / circleSize); 

    for (let y = 0; y < maxY; y++) {
      for (let x = 0; x < maxX; x++) {
        ctx.fillStyle = circleColor;
        ctx.beginPath();
        ctx.arc(x * circleSize * 2 + circleSize, y * circleSize * 2 + circleSize, circleSize, 0, 2 * Math.PI, true);
        ctx.closePath();
        ctx.fill();
      }
    }
}

That’s how easy it is to create dynamic backgrounds using the CSS Paint API.

This example on CodePen has two different backgrounds for desktop and mobile devices.

The trick is to change the variable values inside a media query.

@media screen and (max-width:600px) {
  div#bubble-background {
    --bubble-size: 20;
    --bubble-color: green; 
  }
}

Isn’t that cool? Imagine having static images — you need to have two different images hosted on a server to create those backgrounds. With the CSS Paint API, we can create an endless number of beautiful graphics.

Creating randomly generated backgrounds

Now that you’re comfortable using the CSS Paint API, let’s explore how we can create randomly generated backgrounds using the CSS Paint API.

The Math.random() function is the key to making randomly generated backgrounds.

Math.random()
// returns a float number inclusive of 0 and exclusive of 1

Here we are carrying out roughly the same process as we did earlier; the only difference is that we are using the Math.random function in the paint() method.

Let’s create a background with a random gradient.

body {
  width:100%;
  height:100%;
  background-image: paint(randomBackground);
}


registerPaint('randomBackground', class {
  paint(ctx, geom) {
    const color1 = getRandomHexColor();
    const color2 = getRandomHexColor();

    const gradient = ctx.createLinearGradient(0, 0, geom.width, 0);
    gradient.addColorStop(0, color1);
    gradient.addColorStop(1, color2);

    ctx.fillStyle = gradient;
    ctx.fillRect(0, 0, geom.width, geom.height);
  }
})

function getRandomHexColor() {
  return '#'+ Math.floor(Math.random() * 16777215).toString(16)
}

The getRandomHexColor function does the math part to create a random hex color. See this helpful tutorial for more details about how this works.

Here’s the final result of our random background. If you reload the page, you’ll see random gradients, which you can use to make unique and interesting webpages.

You’ll also notice that the colors change when you resize the browser window. That’s because the browser rerenders the background by calling the paint() method with different geom values upon resizing.

Although Math.random merely generates a simple, random number, it’s the most important function to know when creating any random background. The range of awesome things you can build using this method is limited only by your imagination.

Browser compatibility

As amazing as the CSS Paint API is, browser compatibility can be an issue. Only the most recent browser versions support it. Here’s the browser compatibility data from Is Houdini Ready Yet? as of this writing.

Judging by this data, it’s not yet a good idea to use Houdini in production. However, the Google Chrome Labs team created a polyfill that makes the CSS Paint API work in most browsers. Nevertheless, be sure to test dynamic backgrounds on all major browsers before using it in production.

Detecting browser support

Here’s how to detect browser support in JavaScript:

if ('paintWorklet' in CSS) {
  CSS.paintWorklet.addModule('bubble-paint.js');
}

And in CSS:

@supports (background: paint(id)) {
  div#bubble-background {
    width:400px;
    height:200px;
    background-image: paint(bubblePaint);
  }
}
>

Fallback

CSS fallback properties help improve browser support.

aside {
  background-image: url('/path/to/static/image');
  background-image: paint(bubblePaint);
}

Browsers that don’t support the paint() function won’t recognize that syntax. Therefore, it will ignore the second one and load the URL. Browsers that support it will understand both syntaxes, but the second one will override the first.

Other interesting use cases for CSS Paint API

Below are some other useful and exciting ways to use the CSS Paint API.

Image placeholder

With the CSS Paint API, we can draw a placeholder to display while an image is loading. This requires both Houdini’s new CSS Properties and the CSS Paint API.

Note that only a few browsers support the <image> syntax for CSS Properties, so it might not work in your browser.

Brush stroke background

I’ve seen countless business websites that use brush strokes to emphasis their marketing keywords. While it’s possible to create brush strokes using canvas, it’s much easier with the CSS Paint API.

Since it’s only CSS, you can change the variable and reuse the brush stroke as needed.

.another-brushstroke {
  --brush-color: #fff;
  background-image: paint(brushstroke);
}

Conclusion

In this guide, we covered the basics of the CSS Paint API and explored how to use it with some examples. You should now have the background you need to create more creative and dynamic images with this new API. Although we focused on background-image, you can use the paint() function in other properties too (e.g., border-image). The CSS Paint API, along with the other CSS Houdini features, represents the future of CSS, so now’s the time to get started.

#css #api #web-development

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PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
2. The Tumblr API console at https://api.tumblr.com/console
3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

• state - a string, the state of the post. Supported types are published, draft, queue, private
• tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
• tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
• date - a string, the customized GMT that you want
• format - a string, the format that your post is in. Support types are html or markdown
• slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

• title - a string, the title of post that you want. Supports HTML entities.
• url - a string, the url that you want to create a link post for.
• description - a string, the desciption of the link that you have

#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.

skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys

Public ASX100 APIs: The Essential List

We’ve conducted some initial research into the public APIs of the ASX100 because we regularly have conversations about what others are doing with their APIs and what best practices look like. Being able to point to good local examples and explain what is happening in Australia is a key part of this conversation.

Method

The method used for this initial research was to obtain a list of the ASX100 (as of 18 September 2020). Then work through each company looking at the following:

1. Whether the company had a public API: this was found by googling “[company name] API” and “[company name] API developer” and “[company name] developer portal”. Sometimes the company’s website was navigated or searched.
2. Some data points about the API were noted, such as the URL of the portal/documentation and the method they used to publish the API (portal, documentation, web page).
3. Observations were recorded that piqued the interest of the researchers (you will find these below).
4. Other notes were made to support future research.
5. You will find a summary of the data in the infographic below.

Data

With regards to how the APIs are shared:

• Documentation is where just the Swagger/OpenAPI docs are hosted somewhere. e.g. https://api.linkgroup.com/member/docs/index
• Portal is where an interactive portal has been developed. e.g. https://dev.telstra.com/
• The web page is where the company has a single page that isn’t interactive explaining their Public APIs. e.g. https://www.bendigoadelaide.com.au/banking-products-api.asp

#api #api-development #api-analytics #apis #api-integration #api-testing #api-security #api-gateway

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

• Lists
• Sets
• Dictionaries
• User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

• Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
• Strings
• Tuples
• Frozen Sets
• User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

• Identity – This refers to the address that the object refers to in the computer’s memory.
• Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
• Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

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Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10
 

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick check – Python Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

Mutable Object	Immutable Object
State of the object can be modified after it is created.	State of the object can’t be modified once it is created.
They are not thread safe.	They are thread safe
Mutable classes are not final.	It is important to make the class final before creating an immutable object.

2. What are the mutable and immutable data types in Python?

• Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

• Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

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Original article source at: https://www.mygreatlearning.com

#python 

Drawing Graphics with the CSS Paint API

CSS Paint is an API that allows developers to programatically generate and draw graphics where CSS expects an image.

It is part of CSS Houdini, an umbrella term for seven new low-level APIs that expose different parts of the CSS engine and allows developers to extend CSS by hooking into the styling and layout process of a browser’s rendering engine.

It enables developers to write code the browser can parse as CSS, thereby creating new CSS features without waiting for them to be implemented natively in browsers.

Today we will explore two particular APIs, that are part of the CSS Houdini umbrella:

1. CSS Paint, which at the time of writing this article, has been fully implemented in Chrome, Opera and Edge and is available in Firefox and Safari via a polyfill.
2. CSS Properties and Values API, that will allow us to explicitly define our CSS variables, their initial values, what type of values they support and whether these variables can be inherited.

CSS Paint provides us with ability to render graphics using a PaintWorklet, a stripped down version of the CanvasRenderingContext2D. The major differences are:

• No support for text rendering
• No direct pixel access / manipulation

With these two omissions in mind, anything you can draw using canvas2d, you can draw on a regular DOM element using the CSS Paint API. For those of you who have done any graphics using canvas2d, you should be right at home.

Furthermore, we as developers have the ability to pass CSS variables as inputs to our PaintWorklet and control its presentation using custom predefined attributes.

This allows for a high degree of customisation, even by design people who may not be necessarily familiar with Javascript.

You can see more examples here and here. And with that out of the way, let’s get to coding!

Simplest example: two diagonal lines

Let’s create a CSS paintlet, that once loaded, will draw two diagonal lines across the surface of the DOM element we apply it to. The paintlet drawing surface size will adapt to the width and height of the DOM element and we will be able to control the diagonal line thickness by passing in a CSS variable.

• Creating our PaintWorklet
• Registering our CSS PaintWorklet
• Referencing our paintlet as a CSS background
• Particle Connections
• Line Loop
• Noise Button
• Curvy dividers

#tutorials #css houdini #css paint api #css #api