Table of contents

  1. How to implement custom indentation when pretty-printing with the JSON module in python?
  2. How to build URLs in Python with the standard library?
  3. How to implement the "Wave Collapse Function" algorithm in Python

How to implement custom indentation when pretty-printing with the JSON module in python?

The json module in Python provides a way to customize the indentation when pretty-printing JSON data using the indent parameter of the json.dump() and json.dumps() functions. You can specify the number of spaces or a custom string to use for indentation.

Here's how you can implement custom indentation when pretty-printing with the json module:

import json

# Create a Python dictionary to be pretty-printed
data = {
    "name": "John Doe",
    "age": 30,
    "city": "New York",
    "pets": ["dog", "cat"],

# Customize the indentation with a custom string (e.g., four spaces)
custom_indent = "    "

# Serialize the data to a JSON-formatted string with custom indentation
json_string = json.dumps(data, indent=custom_indent)

# Print the JSON string with custom indentation

In this example, we:

  1. Create a Python dictionary called data.

  2. Customize the indentation by setting the custom_indent variable to four spaces.

  3. Use json.dumps() to serialize the data dictionary into a JSON-formatted string and specify the indent parameter with the custom_indent variable to apply custom indentation.

  4. Print the JSON string with custom indentation.

The output will be:

    "name": "John Doe",
    "age": 30,
    "city": "New York",
    "pets": [

You can customize the custom_indent variable to use any string or number of spaces you prefer for indentation.

How to build URLs in Python with the standard library?

You can build URLs in Python using the urllib.parse module, which is part of the Python standard library. This module provides functions for parsing and constructing URLs according to the RFC 3986 standard. Here's how you can build URLs using urllib.parse:

  1. Import the urllib.parse module:

    from urllib.parse import urlunparse, urlencode
  2. Use the urlunparse() function to construct a URL from its components. The components are a tuple with the following elements:

    • Scheme (e.g., "http", "https")
    • Netloc (e.g., "", "")
    • Path (e.g., "/path/to/resource")
    • Params (e.g., "param=value")
    • Query (e.g., "name=John&age=30")
    • Fragment (e.g., "section1")

    Here's an example:

    url = urlunparse(("https", "", "/path/to/resource", "", "name=John&age=30", "section1"))

    This will output:
  3. If you need to add query parameters to your URL, you can use the urlencode() function to encode a dictionary of parameters and append it to the URL:

    base_url = ""
    params = {
        "name": "John",
        "age": 30
    query_string = urlencode(params)
    url = f"{base_url}?{query_string}"

    This will produce the same URL as in the previous example:

By using the urllib.parse module's functions, you can easily construct URLs with the appropriate components and query parameters in a standardized way.

How to implement the "Wave Collapse Function" algorithm in Python

The "Wave Function Collapse" (WFC) algorithm is a technique used for procedural generation of patterns based on input patterns and their constraints. It's often used for generating textures, terrain, levels, and other content in video games and simulations. The algorithm operates by iteratively collapsing the possibilities for each cell based on its neighbors' constraints.

Implementing the Wave Function Collapse algorithm from scratch can be complex, but I can provide you with a high-level overview of the steps involved:

  1. Input Patterns and Constraints: Define a set of input patterns that you want to use as sources for generating the output. These patterns could be tiles, textures, or other elements. Each pattern should be associated with a set of constraints based on its neighboring patterns.

  2. Grid Representation: Create a grid that represents the output you want to generate. Each cell in the grid will eventually contain a chosen pattern. Initialize each cell with all possible patterns.

  3. Iterative Collapse: Iterate through each cell in the grid and collapse its possibilities based on the constraints from its neighbors. Choose a pattern from the possibilities that satisfies the constraints. This step reduces the possibilities for neighboring cells.

  4. Propagation and Backtracking: As you collapse cells, propagate constraints to neighboring cells and iteratively update their possibilities. If a contradiction arises, you may need to backtrack to a previous state and try different possibilities.

  5. Completion and Sampling: Repeat the iterative process until all cells have been collapsed or a satisfying solution is found. Once the grid is filled with patterns, you can sample it to generate the final output.

  6. Optional Enhancements: Depending on your implementation, you can introduce additional features like priority ordering of cell collapses, enforcing certain constraints, handling edge cases, and optimizing the algorithm's performance.

Due to its complexity, implementing the WFC algorithm from scratch can be quite involved. If you're interested in using this algorithm, you might consider using existing libraries or frameworks that provide implementations of the Wave Function Collapse algorithm. Some popular libraries include "WFC-Texture-Packer" and "WaveFunctionCollapse".

Keep in mind that WFC is a research-intensive topic, and while the above overview provides a basic idea of the algorithm, the actual implementation details can vary based on the specific use case and constraints you're working with.

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