def __init__(self, pgf, args): self.voice = None iLang = args.input.capitalize() oLang = args.output.capitalize() self.gf1 = GF(pgf, iLang, 'Sage', 'Command', lexer=commandsLex) self.gf2 = GF(pgf, 'Sage', oLang, 'Answer') self.args = args self.environment = ContextStack() self.environment.new('toplevel') self.environment.gf1 = self.gf1 # From prompt to change self.environment.kernel = None # to be set on evaluation
def gen_q(data_ndarray, ndim): """ generate q using GF^8 :param data_ndarray: the data ndarray :param ndim: real dim :return: q_ndarray with shape=(1, byte_ndarray.shape[1]) """ transposed = np.transpose(data_ndarray) # print(data_ndarray.shape) get_logger().info('transposed\n{}'.format(transposed)) gf = GF() q_list = [] for _1darray in transposed: bv_list = [] for i, arr_val in enumerate(_1darray): res_i = gf.multiply(gf.generator[i % gf.circle], int(arr_val)) # print('i={}, arr_val={}, res_i={}'.format(i, arr_val, res_i)) bv_list.append(res_i) # map(lambda i: print(i), bv_list) q_value = reduce(operator.xor, bv_list) q_list.append(q_value) arr = np.array(q_list, ndmin=ndim, dtype=config.BYTE_TYPE) get_logger().info("arr={}".format(arr)) # assert arr.shape[1] == data_ndarray.shape[1] return arr
def gf_a_multiply_list(a, l): """ multiplication of a and l :param a: scala type :param l: :return: list of int """ gf = GF() return [gf.multiply(int(i), a) for i in l]
def __init__(self, N): assert 4 <= N super(RAID6, self).__init__(N) # gf object self.gf = GF()