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()