I’m working with tetris pieces.
The pieces are defined with coordinates, where each piece has an origin block (0,0)
So an L piece could be defined as [(0,0), (0,1), (0,2), (1,2)]
as well as [(0,-1), (0,0), (0,1), (1,1)]
depending on where you place the origin block.
I want to check whether a set of coordinates A e.g. [(50,50), (50,51), (50,52), (51,52)]
matches the shape of a given tetris piece B.
I’m currently using numpy to take away one of the A values from every value in A to reach relative coordinates, then compare with B. The ordering of A will always been in increasing order, but is not guarenteed to match the ordering of B. B is stored in a list with other tetris pieces, and throughout the program, it's origin block will remain the same. This method below seems inefficient and doesn’t account for rotations / reflections of B.
def isAinB(A,B): # A and B are numpy arrays
for i in range(len(A)):
matchCoords = A - A[i]
setM = set([tuple(x) for x in matchCoords])
setB = set([tuple(x) for x in B])
if setM == setB: # Sets are used here because the ordering of M and B are not guarenteed to match
return True
return False
Is there an efficient method / function to implement this? (Accounting for rotations and reflections aswell if possible)