Asurmen Proxy

The current version of Skype does not have built-in functionality to work with proxies. That is, it must be configured at the operating system level. The messenger is available for Linux, Windows, MacOS and mobile platforms.

Parsing math expressions correctly involves converting mathematical expressions from their human-readable form into a format that a computer can understand and evaluate. A common approach is to use a parser or library designed for mathematical expressions.

In Python, you can use the sympy library, which provides powerful symbolic mathematics capabilities, including expression parsing and evaluation. Here's an example:

from sympy import sympify, symbols

# Define symbols
x, y = symbols('x y')

# Parse math expressions
expression1 = sympify("2*x + 3*y")
expression2 = sympify("sin(x) + cos(x)")

# Evaluate expressions
result1 = expression1.subs({x: 1, y: 2})
result2 = expression2.subs(x, 0)

print("Result 1:", result1)
print("Result 2:", result2)

In this example, sympify is used to parse the mathematical expressions. You can then substitute values for variables using the subs method.

If you need a more general-purpose parser, you can use the pyparsing library. Here's a basic example:

from pyparsing import Word, nums, operatorPrecedence, opAssoc

# Define grammar for basic math expressions
integer = Word(nums).setParseAction(lambda t: int(t[0]))
variable = Word("xy")
operand = integer | variable
expr = operatorPrecedence(
    operand,
    [
        ("+", 2, opAssoc.LEFT),
        ("-", 2, opAssoc.LEFT),
        ("*", 3, opAssoc.LEFT),
        ("/", 3, opAssoc.LEFT),
    ],
)

# Parse math expressions
expression1 = expr.parseString("2*x + 3*y")
expression2 = expr.parseString("sin(x) + cos(x)")

print("Parsed Expression 1:", expression1)
print("Parsed Expression 2:", expression2)

This example uses pyparsing to define a grammar for basic math expressions with addition, subtraction, multiplication, and division. You can customize the grammar based on your specific needs.

Choose the library that best fits your requirements, whether it's for symbolic mathematics (like sympy) or general-purpose expression parsing (like pyparsing). Always consider error handling and validation when working with user-inputted expressions.

To create your own proxy server, you can use open-source software such as Privoxy or Squid. Here's a step-by-step guide using Privoxy:

Install Privoxy: Download the latest version of Privoxy from the official website (https://www.privoxy.org/download/) and install it on your computer. The installation process varies depending on your operating system.

Configure Privoxy: After installing Privoxy, open the configuration file, usually located at /etc/privoxy/config.txt on Linux or C:\Program Files\Privoxy\config\config.txt on Windows. You can also find the configuration file in the installation directory.

Edit the configuration file: Open the configuration file in a text editor and make the following changes:

Uncomment the following line by removing the # symbol at the beginning:

listen-address 0.0.0.0

Uncomment the following line and change the port number if desired (e.g., 8118):

listen-port 8118

Uncomment the following line to enable HTTPS support:

forward-suffix .privoxy

Add the following line to forward requests to a specific destination server (replace with the desired server's address):

forward-suffix 

Save the configuration file and restart Privoxy: Close the text editor and restart Privoxy to apply the changes. On Linux, you can use the following command:

sudo /etc/init.d/privoxy restart

On Windows, locate the Privoxy service in the Windows Services list and restart it.

Test your proxy server: Open a web browser and configure it to use your new proxy server (e.g., http://localhost:8118). Test by accessing a website to ensure that the proxy server is working correctly.

To keep only unique external links while scraping with Scrapy, you can use a set to track the visited external links and filter out duplicates. Here's an example spider that demonstrates how to achieve this:

import scrapy
from urllib.parse import urlparse, urljoin

class UniqueLinksSpider(scrapy.Spider):
    name = 'unique_links'
    start_urls = ['http://example.com']  # Replace with the starting URL of your choice
    visited_external_links = set()

    def parse(self, response):
        # Extract all links from the current page
        all_links = response.css('a::attr(href)').extract()

        for link in all_links:
            full_url = urljoin(response.url, link)

            # Check if the link is external
            if urlparse(full_url).netloc != urlparse(response.url).netloc:
                # Check if it's a unique external link
                if full_url not in self.visited_external_links:
                    # Add the link to the set of visited external links
                    self.visited_external_links.add(full_url)

                    # Yield the link or process it further
                    yield {
                        'external_link': full_url
                    }

        # Follow links to other pages
        for next_page_url in response.css('a::attr(href)').extract():
            yield scrapy.Request(url=urljoin(response.url, next_page_url), callback=self.parse)

- visited_external_links is a class variable that keeps track of the unique external links across all instances of the spider.
- The parse method extracts all links from the current page.
- For each link, it checks if it is an external link by comparing the netloc (domain) of the current page and the link.
- If the link is external, it checks if it is unique by looking at the visited_external_links set.
- If the link is unique, it is added to the set, and the spider yields the link or processes it further.
- The spider then follows links to other pages, recursively calling the parse method.

Remember to replace the start_urls with the URL from which you want to start scraping.

Audience parsing is the collection of information about users. Most often it is used to get statistical data, to check the server capacity. Sometimes it is also used to compile a database of potential customers.