Create an Automated Build Pipeline for Kotlin in Gitlab.

Leverage Gitlab pipelines and Gradle to automate building and testing your Kotlin code
Over the past few months I’ve been learning a bit of Gradle and Kotlin for a new project at work.
Let’s Go.

#continuous-integration #programming #android #mobile #kotlin

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:

The built-in interactive_console.py tool (if you already have a consumer key & secret)
The Tumblr API console at https://api.tumblr.com/console
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

Tamale Moses

1669003576

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.

Check out this free python certificate course to get started with Python.

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.

Join Great Learning Academy’s free online courses and upgrade your skills today.

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

#python

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

Iñigo Martínez

1637604060

Cree su propia cadena de bloques de criptomonedas en Python

La criptomoneda es una moneda digital descentralizada que utiliza técnicas de cifrado para regular la generación de unidades monetarias y verificar la transferencia de fondos. El anonimato, la descentralización y la seguridad se encuentran entre sus principales características. La criptomoneda no está regulada ni rastreada por ninguna autoridad centralizada, gobierno o banco.

Blockchain, una red descentralizada de igual a igual (P2P), que se compone de bloques de datos, es una parte integral de la criptomoneda. Estos bloques almacenan cronológicamente información sobre transacciones y se adhieren a un protocolo para la comunicación entre nodos y la validación de nuevos bloques. Los datos registrados en bloques no se pueden alterar sin la alteración de todos los bloques posteriores.

En este artículo, explicaremos cómo puede crear una cadena de bloques simple utilizando el lenguaje de programación Python.

Aquí está el plano básico de la clase Python que usaremos para crear la cadena de bloques:

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

Ahora, expliquemos cómo funciona la clase blockchain.

Estructura inicial de la clase de bloque

Aquí está el código para nuestra clase de bloque inicial:

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()

Como puede ver arriba, el constructor de la clase o el método de iniciación ( init ()) anterior toma los siguientes parámetros:

self- al igual que cualquier otra clase de Python, este parámetro se utiliza para hacer referencia a la clase en sí. Se puede acceder a cualquier variable asociada con la clase usándola.

index - se usa para rastrear la posición de un bloque dentro de la cadena de bloques.

previous_hash - solía hacer referencia al hash del bloque anterior dentro de la cadena de bloques.

data—it da detalles de las transacciones realizadas, por ejemplo, la cantidad comprada.

timestamp—it inserta una marca de tiempo para todas las transacciones realizadas.

El segundo método de la clase, compute_hash, se utiliza para producir el hash criptográfico de cada bloque basándose en los valores anteriores.

Como puede ver, importamos el algoritmo SHA-256 al proyecto de cadena de bloques de criptomonedas para ayudar a obtener los valores hash de los bloques.

Una vez que los valores se han colocado dentro del módulo hash, el algoritmo devolverá una cadena de 256 bits que denota el contenido del bloque.

Entonces, esto es lo que le da inmutabilidad a la cadena de bloques. Dado que cada bloque estará representado por un hash, que se calculará a partir del hash del bloque anterior, la corrupción de cualquier bloque de la cadena hará que los otros bloques tengan hash no válidos, lo que provocará la rotura de toda la red blockchain.

Construyendo la Cadena

Todo el concepto de una cadena de bloques se basa en el hecho de que los bloques están "encadenados" entre sí. Ahora, crearemos una clase de blockchain que desempeñará el papel fundamental de administrar toda la cadena.

Mantendrá los datos de las transacciones e incluirá otros métodos auxiliares para completar varios roles, como agregar nuevos bloques.

Hablemos de los métodos de ayuda.

Agregar el método constructor

Aquí está el código:

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

El método constructor init () es lo que crea una instancia de la cadena de bloques.

Aquí están los roles de sus atributos:

self.chain : esta variable almacena todos los bloques.

self.current_data : esta variable almacena información sobre las transacciones en el bloque.

self.build_genesis () : este método se utiliza para crear el bloque inicial en la cadena.

Construyendo el Bloque Génesis

El build_genesis()método se utiliza para crear el bloque inicial en la cadena, es decir, un bloque sin predecesores. El bloque de génesis es lo que representa el comienzo de la cadena de bloques.

Para crearlo, llamaremos al build_block()método y le daremos algunos valores predeterminados. A los parámetros proof_numbery se previous_hashles asigna un valor de cero, aunque puede darles el valor que desee.

Aquí está el código:

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

Confirmación de la validez de Blockchain

El confirm_validitymétodo es fundamental para examinar la integridad de la cadena de bloques y asegurarse de que falten inconsistencias.

Como se explicó anteriormente, los hash son fundamentales para darse cuenta de la seguridad de la cadena de bloques de criptomonedas, porque cualquier ligera alteración en un objeto resultará en la creación de un hash completamente diferente.

Por lo tanto, el confirm_validitymétodo utiliza una serie de declaraciones if para evaluar si el hash de cada bloque se ha visto comprometido.

Además, también compara los valores hash de cada dos bloques sucesivos para identificar cualquier anomalía. Si la cadena funciona correctamente, devuelve verdadero; de lo contrario, devuelve falso.

Aquí está el código:

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

Declaración de datos de transacciones

El get_datamétodo es importante para declarar los datos de las transacciones en un bloque. Este método toma tres parámetros (información del remitente, información del destinatario y monto) y agrega los datos de la transacción a la lista self.current_data.

Aquí está el código:

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

Efectuar la prueba de trabajo

En la tecnología blockchain, Proof of Work (PoW) se refiere a la complejidad involucrada en la minería o la generación de nuevos bloques en blockchain.

Por ejemplo, el PoW se puede implementar identificando un número que resuelve un problema cada vez que un usuario completa algún trabajo informático. Cualquiera en la red blockchain debería encontrar el número complejo de identificar pero fácil de verificar: este es el concepto principal de PoW.

De esta manera, desalienta el envío de spam y compromete la integridad de la red.

En este artículo, ilustraremos cómo incluir un algoritmo de Prueba de trabajo en un proyecto de criptomoneda blockchain.

Finalizando con el último bloque

Finalmente, el método auxiliar latest_block () se usa para recuperar el último bloque en la red, que en realidad es el bloque actual.

Aquí está el código:

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

Implementando Blockchain Mining

¡Ahora, esta es la sección más emocionante!

Inicialmente, las transacciones se mantienen en una lista de transacciones no verificadas. La minería se refiere al proceso de colocar las transacciones no verificadas en un bloque y resolver el problema de PoW. Puede denominarse el trabajo informático involucrado en la verificación de las transacciones.

Si todo se ha resuelto correctamente, se crea o extrae un bloque y se une con los demás en la cadena de bloques. Si los usuarios han extraído un bloque con éxito, a menudo se les recompensa por utilizar sus recursos informáticos para resolver el problema de PoW.

Aquí está el método de minería en este simple proyecto de cadena de bloques de criptomonedas:

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)

Resumen

Aquí está el código completo para nuestra clase de cadena de bloques de cifrado en 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)

Ahora, intentemos ejecutar nuestro código para ver si podemos generar algunas monedas digitales ...

¡Vaya, funcionó!

Conclusión

¡Eso es!

Esperamos que este artículo le haya ayudado a comprender la tecnología subyacente que impulsa las criptomonedas como Bitcoin y Ethereum.

Acabamos de ilustrar las ideas básicas para mojarse los pies en la innovadora tecnología blockchain. El proyecto anterior aún se puede mejorar incorporando otras características para hacerlo más útil y robusto.

Alexander Karachunov

1637600340

Создайте свой собственный блокчейн криптовалюты на Python

Криптовалюта - это децентрализованная цифровая валюта, в которой используются методы шифрования для регулирования генерации денежных единиц и проверки перевода средств. Анонимность, децентрализация и безопасность - одни из его основных характеристик. Криптовалюта не регулируется и не отслеживается каким-либо централизованным органом, правительством или банком.

Блокчейн, децентрализованная одноранговая (P2P) сеть, состоящая из блоков данных, является неотъемлемой частью криптовалюты. Эти блоки хранят информацию о транзакциях в хронологическом порядке и придерживаются протокола для межузловой связи и проверки новых блоков. Данные, записанные в блоках, не могут быть изменены без изменения всех последующих блоков.

В этой статье мы собираемся объяснить, как создать простой блокчейн с помощью языка программирования Python.

Вот базовый план класса Python, который мы будем использовать для создания блокчейна:

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

Теперь давайте объясним, как работает класс блокчейна.

Начальная структура класса блоков

Вот код нашего начального класса блока:

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()

Как вы можете видеть выше, конструктор класса или метод инициации ( init ()) выше принимает следующие параметры:

self- как и любой другой класс Python, этот параметр используется для ссылки на сам класс. С его помощью можно получить доступ к любой переменной, связанной с классом.

index - он используется для отслеживания положения блока в цепочке блоков.

previous_hash - он использовался для ссылки на хэш предыдущего блока в цепочке блоков.

data—it предоставляет подробную информацию о проведенных транзакциях, например, купленную сумму.

timestamp—it вставляет отметку времени для всех выполненных транзакций.

Второй метод в классе, compute_hash, используется для создания криптографического хэша каждого блока на основе вышеуказанных значений.

Как видите, мы импортировали алгоритм SHA-256 в проект блокчейна криптовалюты, чтобы помочь в получении хэшей блоков.

Как только значения будут помещены в модуль хеширования, алгоритм вернет 256-битную строку, обозначающую содержимое блока.

Итак, это то, что дает неизменяемость блокчейна. Поскольку каждый блок будет представлен хешем, который будет вычисляться из хеша предыдущего блока, повреждение любого блока в цепочке приведет к тому, что другие блоки будут иметь недопустимые хеши, что приведет к поломке всей сети блокчейна.

Построение цепочки

Вся концепция блокчейна основана на том факте, что блоки «связаны» друг с другом. Теперь мы создадим класс цепочки блоков, который будет играть важную роль в управлении всей цепочкой.

Он будет хранить данные транзакций и включать другие вспомогательные методы для выполнения различных ролей, таких как добавление новых блоков.

Поговорим о вспомогательных методах.

Добавление метода конструктора

Вот код:

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

Метод конструктора init () - это то, что создает экземпляр блокчейна.

Вот роли его атрибутов:

self.chain - в этой переменной хранятся все блоки.

self.current_data - в этой переменной хранится информация о транзакциях в блоке.

self.build_genesis () - этот метод используется для создания начального блока в цепочке.

Создание блока генезиса

build_genesis()Метод используется для создания начального блока в цепочке, то есть, блок без каких - либо предшественников. Блок генезиса - это то, что представляет собой начало блокчейна.

Чтобы создать его, мы вызовем build_block()метод и дадим ему значения по умолчанию. Оба параметра proof_numberи previous_hashимеют нулевое значение, хотя вы можете присвоить им любое значение, которое пожелаете.

Вот код:

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

Подтверждение действительности блокчейна

Этот confirm_validityметод имеет решающее значение для проверки целостности цепочки блоков и проверки отсутствия несоответствий.

Как объяснялось ранее, хэши имеют решающее значение для обеспечения безопасности блокчейна криптовалюты, потому что любое небольшое изменение в объекте приведет к созданию совершенно другого хэша.

Таким образом, confirm_validityметод использует серию операторов if для оценки того, был ли скомпрометирован хэш каждого блока.

Кроме того, он также сравнивает хеш-значения каждых двух последовательных блоков для выявления любых аномалий. Если цепочка работает правильно, возвращается истина; в противном случае возвращается false.

Вот код:

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

Объявление данных транзакций

get_dataМетод имеет важное значение в объявлении данных об операциях на блоке. Этот метод принимает три параметра (информацию об отправителе, информацию о получателе и сумму) и добавляет данные транзакции в список self.current_data.

Вот код:

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

Выполнение доказательства работы

В технологии блокчейн Proof of Work (PoW) относится к сложности, связанной с майнингом или генерацией новых блоков в блокчейне.

Например, PoW может быть реализован путем определения числа, которое решает проблему всякий раз, когда пользователь выполняет некоторую вычислительную работу. Любой в сети блокчейн должен найти номер сложным для идентификации, но легким для проверки - это основная концепция PoW.

Таким образом, это препятствует распространению спама и нарушению целостности сети.

В этой статье мы покажем, как включить алгоритм Proof of Work в проект криптовалюты на блокчейне.

Завершение с последним блоком

Наконец, вспомогательный метод latest_block () используется для получения последнего блока в сети, который на самом деле является текущим блоком.

Вот код:

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

Внедрение Blockchain Mining

Теперь это самый интересный раздел!

Изначально транзакции хранятся в списке непроверенных транзакций. Майнинг относится к процессу размещения непроверенных транзакций в блоке и решения проблемы PoW. Это можно назвать вычислительной работой, связанной с проверкой транзакций.

Если все было правильно выяснено, блок создается или добывается и объединяется вместе с другими в цепочке блоков. Если пользователи успешно добыли блок, они часто получают вознаграждение за использование своих вычислительных ресурсов для решения проблемы PoW.

Вот метод майнинга в этом простом проекте блокчейна криптовалюты:

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)

Резюме

Вот весь код нашего класса криптоблокчейна на 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)

Теперь давайте попробуем запустить наш код, чтобы посмотреть, сможем ли мы сгенерировать несколько цифровых монет ...

Вау, сработало!

Заключение

Вот и все!

Мы надеемся, что эта статья помогла вам понять базовую технологию, на которой работают такие криптовалюты, как Биткойн и Эфириум.

Мы просто проиллюстрировали основные идеи, как сделать ваши ноги влажными в инновационной технологии блокчейн. Вышеупомянутый проект все еще можно улучшить, добавив другие функции, чтобы сделать его более полезным и надежным.

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Build Your Own Cryptocurrency Blockchain in Python

Initial Structure of the Block Class

Building the Chain

Adding the Constructor Method

Building the Genesis Block

Confirming Validity of the Blockchain

Declaring Data of Transactions

Effecting the Proof of Work

Finalizing With the Last Block

Implementing Blockchain Mining

Summary

Conclusion

Learn More

Cree su propia cadena de bloques de criptomonedas en Python

Estructura inicial de la clase de bloque

Construyendo la Cadena

Agregar el método constructor

Construyendo el Bloque Génesis

Confirmación de la validez de Blockchain

Declaración de datos de transacciones

Efectuar la prueba de trabajo

Finalizando con el último bloque

Implementando Blockchain Mining

Resumen

Conclusión

Создайте свой собственный блокчейн криптовалюты на Python

Начальная структура класса блоков

Построение цепочки

Добавление метода конструктора

Создание блока генезиса

Подтверждение действительности блокчейна

Объявление данных транзакций

Выполнение доказательства работы

Завершение с последним блоком

Внедрение Blockchain Mining

Резюме

Заключение