Nxnxn Rubik 39scube Algorithm Github Python Full _verified_ 💯

The most intuitive approach represents each of the 6 faces as an two-dimensional NumPy array.

: Match all edge pieces of the same color into "edge groups".

The solver uses a hybrid strategy:

import numpy as np class NxNCube: def __init__(self, n: int): if n < 1: raise ValueError("Cube size N must be at least 1.") self.n = n self.faces = 'U': np.full((n, n), 0), # White 'D': np.full((n, n), 1), # Yellow 'F': np.full((n, n), 2), # Green 'B': np.full((n, n), 3), # Blue 'L': np.full((n, n), 4), # Orange 'R': np.full((n, n), 5) # Red def copy(self): """Creates a deep copy of the current cube state.""" new_cube = NxNCube(self.n) for face in self.faces: new_cube.faces[face] = np.copy(self.faces[face]) return new_cube def is_solved(self) -> bool: """Checks if all faces contain uniform values.""" for face in self.faces.values(): if not np.all(face == face[0, 0]): return False return True def rotate_face_clockwise(self, face: str): """Rotates the outer boundary layer of a target face.""" self.faces[face] = np.rot90(self.faces[face], -1) def rotate_slice(self, axis: str, layer: int, clockwise: bool = True): """ Rotates a specific layer index along a given axis. layer: 0 is the outermost face, n-1 is the opposite face. axis: 'x' (L/R), 'y' (U/D), 'z' (F/B) """ k = -1 if clockwise else 1 n = self.n idx = layer if axis == 'y': # Horizontal slices (U to D) if idx == 0: self.faces['U'] = np.rot90(self.faces['U'], k) if idx == n - 1: self.faces['D'] = np.rot90(self.faces['D'], -k) # Row tracking across adjacent faces: F -> L -> B -> R -> F if clockwise: temp = np.copy(self.faces['F'][idx, :]) self.faces['F'][idx, :] = self.faces['R'][idx, :] self.faces['R'][idx, :] = self.faces['B'][idx, :] self.faces['B'][idx, :] = self.faces['L'][idx, :] self.faces['L'][idx, :] = temp else: temp = np.copy(self.faces['F'][idx, :]) self.faces['F'][idx, :] = self.faces['L'][idx, :] self.faces['L'][idx, :] = self.faces['B'][idx, :] self.faces['B'][idx, :] = self.faces['R'][idx, :] self.faces['R'][idx, :] = temp elif axis == 'x': # Vertical slices (L to R) if idx == 0: self.faces['L'] = np.rot90(self.faces['L'], k) if idx == n - 1: self.faces['R'] = np.rot90(self.faces['R'], -k) # Column tracking: U -> F -> D -> B -> U (Note: Back face orientation is inverted) if clockwise: temp = np.copy(self.faces['U'][:, idx]) self.faces['U'][:, idx] = np.flip(self.faces['B'][:, n - 1 - idx]) self.faces['B'][:, n - 1 - idx] = np.flip(self.faces['D'][:, idx]) self.faces['D'][:, idx] = self.faces['F'][:, idx] self.faces['F'][:, idx] = temp else: temp = np.copy(self.faces['U'][:, idx]) self.faces['U'][:, idx] = self.faces['F'][:, idx] self.faces['F'][:, idx] = self.faces['D'][:, idx] self.faces['D'][:, idx] = np.flip(self.faces['B'][:, n - 1 - idx]) self.faces['B'][:, n - 1 - idx] = np.flip(temp) elif axis == 'z': # Depth slices (F to B) if idx == 0: self.faces['F'] = np.rot90(self.faces['F'], k) if idx == n - 1: self.faces['B'] = np.rot90(self.faces['B'], -k) # Boundary tracking between U, R, D, L if clockwise: temp = np.copy(self.faces['U'][n - 1 - idx, :]) self.faces['U'][n - 1 - idx, :] = np.flip(self.faces['L'][:, n - 1 - idx]) self.faces['L'][:, n - 1 - idx] = self.faces['D'][idx, :] self.faces['D'][idx, :] = np.flip(self.faces['R'][:, idx]) self.faces['R'][:, idx] = temp else: temp = np.copy(self.faces['U'][n - 1 - idx, :]) self.faces['U'][n - 1 - idx, :] = self.faces['R'][:, idx] self.faces['R'][:, idx] = np.flip(self.faces['D'][idx, :]) self.faces['D'][idx, :] = self.faces['L'][:, n - 1 - idx] self.faces['L'][:, n - 1 - idx] = np.flip(temp) Use code with caution. 4. Understanding the Reduction Solver Algorithm cube where nxnxn rubik 39scube algorithm github python full

This article explores the best , focusing on Python , to help you build or understand a full solver. 1. Why Use Python for Rubik's Cube Algorithms?

The basic idea is to systematically simplify the cube step-by-step until it behaves exactly like a standard 3x3:

def fix_oll_parity(cube): if cube.n % 2 == 0 and cube.has_oll_parity(): cube.apply_moves("2R2 B2 U2 2L U2 2R' U2 2R U2 F2 2R F2 2L' B2 2R2") The most intuitive approach represents each of the

You can prototype complex algorithms quickly.

To make the Python solver highly efficient (comparable to top GitHub projects), a hybrid search strategy is employed:

To build a robust program, we separate the logic into three distinct components: layer: 0 is the outermost face, n-1 is the opposite face

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A prime symbol ( ' ) denotes a counter-clockwise turn, while a 2 denotes a double turn ( 180∘180 raised to the composed with power 3. Production-Ready Python Implementation

def group_pieces(pieces): # Group pieces by color and position groups = {} for piece in pieces: color = piece.color position = piece.position if color not in groups: groups[color] = [] groups[color].append(position) return groups

This library is perfect for algorithmic research, move analysis, or building educational tools around the cube.

Easy to feed in a cube state via a string. Installation and Usage: