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Simulate Conway's Game of Life with evolving grid cells based on simple rules for dynamic patterns and designs.
The GameOfLife
node simulates Conway's Game of Life, a cellular automaton devised by mathematician John Conway. This node generates a grid of cells that evolve over a series of steps based on a set of simple rules. Each cell can be either "on" (alive) or "off" (dead), and its state in the next step is determined by the states of its eight neighbors. This node is particularly useful for creating dynamic, evolving patterns and can be used in various artistic and computational contexts to generate intricate and visually appealing designs. The main goal of this node is to provide a flexible and customizable way to simulate the Game of Life, allowing you to control the grid size, cell size, initial conditions, and evolution steps.
The width parameter specifies the number of cells along the horizontal axis of the grid. It determines the overall width of the simulation area. A larger width will result in a wider grid, allowing for more complex patterns to emerge. There is no strict minimum or maximum value, but typical values range from 10 to 1000. The default value is usually set to 100.
The height parameter specifies the number of cells along the vertical axis of the grid. It determines the overall height of the simulation area. A larger height will result in a taller grid, allowing for more complex patterns to emerge. There is no strict minimum or maximum value, but typical values range from 10 to 1000. The default value is usually set to 100.
The cell_size parameter defines the size of each cell in the grid. This affects the visual representation of the grid, with larger cell sizes making individual cells more prominent. The minimum value is 1, and there is no strict maximum value, but typical values range from 1 to 50. The default value is usually set to 10.
The seed parameter is used to initialize the random number generator for the grid's initial state. By setting a specific seed value, you can ensure that the same initial grid configuration is generated each time, which is useful for reproducibility. The seed can be any integer value. The default value is typically set to 42.
The threshold parameter determines the probability that a cell will be "on" (alive) in the initial grid configuration. It is a value between 0 and 1, where a higher threshold means more cells will start in the "on" state. The default value is usually set to 0.5.
The steps parameter specifies the number of iterations or generations the simulation will run. Each step updates the grid based on the rules of the Game of Life. The minimum value is 1, and there is no strict maximum value, but typical values range from 1 to 1000. The default value is usually set to 100.
The optional_start parameter allows you to provide a custom initial grid configuration. If provided, this grid will be used as the starting point for the simulation instead of a randomly generated grid. This parameter is optional and can be left as None if you prefer a random start.
The grid output parameter provides the final state of the grid after all the specified steps have been executed. It is a 2D array where each element represents the state of a cell, with 1 indicating "on" (alive) and 0 indicating "off" (dead). This output is useful for visualizing the end result of the simulation.
The turn_off output parameter provides a 2D array indicating which cells turned from "on" to "off" during each step of the simulation. This can be used to track changes and understand the evolution of the grid over time.
The turn_on output parameter provides a 2D array indicating which cells turned from "off" to "on" during each step of the simulation. This can be used to track changes and understand the evolution of the grid over time.
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