Chih- Yu Lin

1621655487

How to Build Bioinformatics Tools

In this video, I talk about the importance of bioinformatics tools, what it takes to create a bioinformatic tool and finally showing you how to create a bioinformatic tool from scratch in Python. This video is based on the plenary lecture and practical workshop that I delivered at the International E-Workshop on Machine Learning Applications in Drug Discovery: Basic to Advanced (MLADDBA-2021) that was organized by the Department of Biotechnology, Vignan University, India (May 17 and 18, 2021).

⭕ Timeline
0:00 Introduction
1:24 Lecture about bioinformatics tool
57:38 Tutorial on building bioinformatics tool

⭕ Links for this video

Subscribe: https://www.youtube.com/c/DataProfessor/featured

#python #machine-learning

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How to Build Bioinformatics Tools

50+ Useful DevOps Tools

The article comprises both very well established tools for those who are new to the DevOps methodology.

What Is DevOps?

The DevOps methodology, a software and team management approach defined by the portmanteau of Development and Operations, was first coined in 2009 and has since become a buzzword concept in the IT field.

DevOps has come to mean many things to each individual who uses the term as DevOps is not a singularly defined standard, software, or process but more of a culture. Gartner defines DevOps as:

“DevOps represents a change in IT culture, focusing on rapid IT service delivery through the adoption of agile, lean practices in the context of a system-oriented approach. DevOps emphasizes people (and culture), and seeks to improve collaboration between operations and development teams. DevOps implementations utilize technology — especially automation tools that can leverage an increasingly programmable and dynamic infrastructure from a life cycle perspective.”

As you can see from the above definition, DevOps is a multi-faceted approach to the Software Development Life Cycle (SDLC), but its main underlying strength is how it leverages technology and software to streamline this process. So with the right approach to DevOps, notably adopting its philosophies of co-operation and implementing the right tools, your business can increase deployment frequency by a factor of 30 and lead times by a factor of 8000 over traditional methods, according to a CapGemini survey.

The Right Tools for the Job

This list is designed to be as comprehensive as possible. The article comprises both very well established tools for those who are new to the DevOps methodology and those tools that are more recent releases to the market — either way, there is bound to be a tool on here that can be an asset for you and your business. For those who already live and breathe DevOps, we hope you find something that will assist you in your growing enterprise.

With such a litany of tools to choose from, there is no “right” answer to what tools you should adopt. No single tool will cover all your needs and will be deployed across a variety of development and Operational teams, so let’s break down what you need to consider before choosing what tool might work for you.

  • Plan and collaborate: Before you even begin the SDLC, your business needs to have a cohesive idea of what tools they’ll need to implement across your teams. There are even DevOps tools that can assist you with this first crucial step.
  • Build: Here you want tools that create identically provisioned environments. The last you need is to hear “But it works for me on my computer”
  • Automation: This has quickly become a given in DevOps, but automation will always drastically increase production over manual methods.
  • Continuous Integration: Tools need to provide constant and immediate feedback, several times a day but not all integrations are implemented equally, will the tool you select be right for the job?
  • Deployment: Deployments need to be kept predictable, smooth, and reliable with minimal risks, automation will also play a big part in this process.

With all that in mind, I hope this selection of tools will aid you as your business continues to expand into the DevOps lifestyle.

Tools Categories List:

Infrastructure As Code

Continuous Integration and Delivery

Development Automation

Usability Testing

Database and Big Data

Monitoring

Testing

Security

Helpful CLI Tools

Development

Visualization

Infrastructure As Code

#AWSCloudFormation

1. AWS CloudFormation

AWS CloudFormation is an absolute must if you are currently working, or planning to work, in the AWS Cloud. CloudFormation allows you to model your AWS infrastructure and provision all your AWS resources swiftly and easily. All of this is done within a JSON or YAML template file and the service comes with a variety of automation features ensuring your deployments will be predictable, reliable, and manageable.

Link: https://aws.amazon.com/cloudformation/

2. Azure Resource Manager

Azure Resource Manager (ARM) is Microsoft’s answer to an all-encompassing IAC tool. With its ARM templates, described within JSON files, Azure Resource Manager will provision your infrastructure, handle dependencies, and declare multiple resources via a single template.

Link: https://azure.microsoft.com/en-us/features/resource-manager/

#Google Cloud Deployment Manager

3. Google Cloud Deployment Manager

Much like the tools mentioned above, Google Cloud Deployment Manager is Google’s IAC tool for the Google Cloud Platform. This tool utilizes YAML for its config files and JINJA2 or PYTHON for its templates. Some of its notable features are synchronistic deployment and ‘preview’, allowing you an overhead view of changes before they are committed.

Link: https://cloud.google.com/deployment-manager/

4. Terraform

Terraform is brought to you by HashiCorp, the makers of Vault and Nomad. Terraform is vastly different from the above-mentioned tools in that it is not restricted to a specific cloud environment, this comes with increased benefits for tackling complex distributed applications without being tied to a single platform. And much like Google Cloud Deployment Manager, Terraform also has a preview feature.

Link: https://www.terraform.io/

#Chef

5. Chef

Chef is an ideal choice for those who favor CI/CD. At its heart, Chef utilizes self-described recipes, templates, and cookbooks; a collection of ready-made templates. Cookbooks allow for consistent configuration even as your infrastructure rapidly scales. All of this is wrapped up in a beautiful Ruby-based DSL pie.

Link: https://www.chef.io/products/chef-infra/

#Ansible

#tools #devops #devops 2020 #tech tools #tool selection #tool comparison

The Best Way to Build a Chatbot in 2021

A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.

With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.

Plan Out the Chatbot’s Purpose

Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.

Some benefits of implementing chatbots include:

  • Generating leads for marketing products and services
  • Improve work capacity when employees cannot answer questions or during non-business hours
  • Reducing errors while providing accurate information to customers or visitors
  • Meeting customer demands through instant communication
  • Alerting customers about their online transactions

Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.

#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot

Riyad Amin

Riyad Amin

1571046022

Build Your Own Cryptocurrency Blockchain in Python

Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.

Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.

In this article, we are going to explain how you can create a simple blockchain using the Python programming language.

Here is the basic blueprint of the Python class we’ll use for creating the blockchain:

class Block(object):
    def __init__():
        pass
    #initial structure of the block class 
    def compute_hash():
        pass
    #producing the cryptographic hash of each block 
  class BlockChain(object):
    def __init__(self):
    #building the chain
    def build_genesis(self):
        pass
    #creating the initial block
    def build_block(self, proof_number, previous_hash):
        pass
    #builds new block and adds to the chain
   @staticmethod
    def confirm_validity(block, previous_block):
        pass
    #checks whether the blockchain is valid
    def get_data(self, sender, receiver, amount):
        pass
    # declares data of transactions
    @staticmethod
    def proof_of_work(last_proof):
        pass
    #adds to the security of the blockchain
    @property
    def latest_block(self):
        pass
    #returns the last block in the chain

Now, let’s explain how the blockchain class works.

Initial Structure of the Block Class

Here is the code for our initial block class:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()

As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:

self — just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.

index — it’s used to track the position of a block within the blockchain.

previous_hash — it used to reference the hash of the previous block within the blockchain.

data—it gives details of the transactions done, for example, the amount bought.

timestamp—it inserts a timestamp for all the transactions performed.

The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.

As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.

Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.

So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.

Building the Chain

The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.

It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.

Let’s talk about the helper methods.

Adding the Constructor Method

Here is the code:

class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()

The init() constructor method is what instantiates the blockchain.

Here are the roles of its attributes:

self.chain — this variable stores all the blocks.

self.current_data — this variable stores information about the transactions in the block.

self.build_genesis() — this method is used to create the initial block in the chain.

Building the Genesis Block

The build_genesis() method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.

To create it, we’ll call the build_block() method and give it some default values. The parameters proof_number and previous_hash are both given a value of zero, though you can give them any value you desire.

Here is the code:

def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
 def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block

Confirming Validity of the Blockchain

The confirm_validity method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.

As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.

Thus, the confirm_validity method utilizes a series of if statements to assess whether the hash of each block has been compromised.

Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.

Here is the code:

def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True

Declaring Data of Transactions

The get_data method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.

Here is the code:

def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True

Effecting the Proof of Work

In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.

For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.

This way, it discourages spamming and compromising the integrity of the network.

In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.

Finalizing With the Last Block

Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.

Here is the code:

def latest_block(self):
        return self.chain[-1]

Implementing Blockchain Mining

Now, this is the most exciting section!

Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.

If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.

Here is the mining method in this simple cryptocurrency blockchain project:

def block_mining(self, details_miner):
            self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)

Summary

Here is the whole code for our crypto blockchain class in Python:

import hashlib
import time
class Block(object):
    def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
        self.index = index
        self.proof_number = proof_number
        self.previous_hash = previous_hash
        self.data = data
        self.timestamp = timestamp or time.time()
    @property
    def compute_hash(self):
        string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
        return hashlib.sha256(string_block.encode()).hexdigest()
    def __repr__(self):
        return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
    def __init__(self):
        self.chain = []
        self.current_data = []
        self.nodes = set()
        self.build_genesis()
    def build_genesis(self):
        self.build_block(proof_number=0, previous_hash=0)
    def build_block(self, proof_number, previous_hash):
        block = Block(
            index=len(self.chain),
            proof_number=proof_number,
            previous_hash=previous_hash,
            data=self.current_data
        )
        self.current_data = []  
        self.chain.append(block)
        return block
    @staticmethod
    def confirm_validity(block, previous_block):
        if previous_block.index + 1 != block.index:
            return False
        elif previous_block.compute_hash != block.previous_hash:
            return False
        elif block.timestamp <= previous_block.timestamp:
            return False
        return True
    def get_data(self, sender, receiver, amount):
        self.current_data.append({
            'sender': sender,
            'receiver': receiver,
            'amount': amount
        })
        return True        
    @staticmethod
    def proof_of_work(last_proof):
        pass
    @property
    def latest_block(self):
        return self.chain[-1]
    def chain_validity(self):
        pass        
    def block_mining(self, details_miner):       
        self.get_data(
            sender="0", #it implies that this node has created a new block
            receiver=details_miner,
            quantity=1, #creating a new block (or identifying the proof number) is awared with 1
        )
        last_block = self.latest_block
        last_proof_number = last_block.proof_number
        proof_number = self.proof_of_work(last_proof_number)
        last_hash = last_block.compute_hash
        block = self.build_block(proof_number, last_hash)
        return vars(block)  
    def create_node(self, address):
        self.nodes.add(address)
        return True
    @staticmethod
    def get_block_object(block_data):        
        return Block(
            block_data['index'],
            block_data['proof_number'],
            block_data['previous_hash'],
            block_data['data'],
            timestamp=block_data['timestamp']
        )
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
    sender="0", #this means that this node has constructed another block
    receiver="LiveEdu.tv", 
    amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)

Now, let’s try to run our code to see if we can generate some digital coins…

Wow, it worked!

Conclusion

That is it!

We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.

We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.

Learn More

Thanks for reading !

Do you have any comments or questions? Please share them below.

#python #cryptocurrency

Sunny  Kunde

Sunny Kunde

1597848060

Top 12 Most Used Tools By Developers In 2020

rameworks and libraries can be said as the fundamental building blocks when developers build software or applications. These tools help in opting out the repetitive tasks as well as reduce the amount of code that the developers need to write for a particular software.

Recently, the Stack Overflow Developer Survey 2020 surveyed nearly 65,000 developers, where they voted their go-to tools and libraries. Here, we list down the top 12 frameworks and libraries from the survey that are most used by developers around the globe in 2020.

(The libraries are listed according to their number of Stars in GitHub)

1| TensorFlow

**GitHub Stars: **147k

Rank: 5

**About: **Originally developed by researchers of Google Brain team, TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art research in ML. It allows developers to easily build and deploy ML-powered applications.

Know more here.

2| Flutter

**GitHub Stars: **98.3k

**Rank: **9

About: Created by Google, Flutter is a free and open-source software development kit (SDK) which enables fast user experiences for mobile, web and desktop from a single codebase. The SDK works with existing code and is used by developers and organisations around the world.


#opinions #developer tools #frameworks #java tools #libraries #most used tools by developers #python tools

Amara  Legros

Amara Legros

1598228110

8 Fun AI Tools Available Online

“AI for fun” — a phrase that we commonly don’t hear in the industry. Artificial intelligence has always been considered a revolutionary technology that has emerged to solve complex real-world problems like high-level computation, omitting manual labour, or data-driven optimisation. However, with its endless possibilities, there are many applications of AI that make this technology more accessible to the average layman person or kids at home.

To get people’s head around this sophisticated technology developers all around the world are continuously developing some fun AI tools that can be easily accessed online to get hands-on. Not only are these AI tools fun but also provide a good understanding of this technology to the users.

Here is a list of 10 exciting artificial intelligence tools that are available online for anyone to have fun with.

#opinions #ai tool online #ai tools #artificial intelligence tools #fun ai tools