Untitled

def onBoard(i, j, image):
    ni = image.shape[0] # number of pixels (height)
    nj = image.shape[1] # number of pixels (width)
    if (i <= ni-1 and i >= 0) and (j <= nj-1 and j >= 0): # You need some more conditions here! 
                                         # We've checked i, but what about j?
        return True
    else:
        return False

#Finish this code
def getNeighborValues(i,j, board):
    # The following list contains the indices of the neighbors for a pixel at (i.j)
    # But remember, we need to check if the neighbor is on the board when we try to get its value!
    neighborhood = [(i-1, j), (i, j-1), (i+1, j), (i, j+1)]
    
    neighbor_values = []
    for neighbor in neighborhood:
        check = onBoard(neighbor[0], neighbor[1], board)

        if check == True:
            neighbor_values.append(board[neighbor])
        else:
            neighbor_values.append(None)
    
    return neighbor_values

def advance_board(board):
    '''
    Advances the game board using the given rules.
    Input: the initial game board.
    Output: the advanced game board
    '''
    
    # create a new array that's just like the original one, but initially set to all zeros (i.e., totally empty)
    new_board = np.zeros_like(board)
    
    # loop over each cell in the board and decide what to do.
    # You'll need two loops here, one nested inside the other.
    for i, ignore in enumerate(board[0]):
        for j, ignore in enumerate(board[1]):
            neighborhood = [(i-1, j), (i, j-1), (i+1, j), (i, j+1)]

            if board[i, j] == 1 and new_board[i, j] != 2:
                new_board[i, j] = 1

            elif board[i,j] == 2:
                neighbor_array = getNeighborValues(i, j, board)
                new_board[i, j] = 0
                for index, value in enumerate(neighbor_array):

                    if value == 0.0:
                        new_board[neighborhood[index][0],neighborhood[index][1]] = 0
                    elif value == 1.0:
                        new_board[neighborhood[index][0],neighborhood[index][1]] = 2

    return new_board