from gnuradio import gr
import math, numpy

class gr_norm(gr.basic_block):
    def __init__(self):
        gr.basic_block.__init__(self, name="normalizer", in_sig=[numpy.complex64], out_sig=[numpy.complex64])

    def general_work(self, input_items, output_items):
        if len(input_items[0]) == 0: return -1
        in0 = input_items[0]
        out = output_items[0]

        out[0] = in0[0]

        self.consume(0, 1)

        return 1

from gnuradio import gr, gr_unittest
import numpy

class qa_norm(gr_unittest.TestCase):

    def setUp(self):
        self.tb = gr.top_block()

    def tearDown(self):
        self.tb = None

    def test_001_gr_norm(self):
        src_data = [ -10, 10, -10j, 10j ]
        expected_result = [ -1, 1, -1j, 1j ]
        src = gr.vector_source_c(src_data)
        nor = gr_norm()
        dst = gr.vector_sink_c()
        self.tb.connect(src, nor)
        self.tb.connect(nor, dst)
        self.tb.run()
        result_data = dst.data()
        self.assertFloatTuplesAlmostEqual(expected_result, result_data, 6)
        
if __name__ == '__main__':
    gr_unittest.main()