diff options
Diffstat (limited to 'gr-digital/examples/berawgn.py')
| -rw-r--r--[-rwxr-xr-x] | gr-digital/examples/berawgn.py | 23 |
1 files changed, 13 insertions, 10 deletions
diff --git a/gr-digital/examples/berawgn.py b/gr-digital/examples/berawgn.py index c47d99174a..886c93bdfe 100755..100644 --- a/gr-digital/examples/berawgn.py +++ b/gr-digital/examples/berawgn.py @@ -32,6 +32,10 @@ Of course, expect the maximum value for BER to be one order of magnitude below what you chose for N_BITS. """ +from __future__ import print_function +from __future__ import division +from __future__ import unicode_literals + import math import numpy @@ -43,13 +47,13 @@ import sys try: from scipy.special import erfc except ImportError: - print "Error: could not import scipy (http://www.scipy.org/)" + print("Error: could not import scipy (http://www.scipy.org/)") sys.exit(1) try: import pylab except ImportError: - print "Error: could not import pylab (http://matplotlib.sourceforge.net/)" + print("Error: could not import pylab (http://matplotlib.sourceforge.net/)") sys.exit(1) # Best to choose powers of 10 @@ -58,7 +62,7 @@ RAND_SEED = 42 def berawgn(EbN0): """ Calculates theoretical bit error rate in AWGN (for BPSK and given Eb/N0) """ - return 0.5 * erfc(math.sqrt(10**(float(EbN0)/10))) + return 0.5 * erfc(math.sqrt(10**(float(EbN0) / 10))) class BitErrors(gr.hier_block2): """ Two inputs: true and received bits. We compare them and @@ -81,7 +85,7 @@ class BitErrors(gr.hier_block2): blocks.unpack_k_bits_bb(bits_per_byte), blocks.uchar_to_float(), blocks.integrate_ff(intdump_decim), - blocks.multiply_const_ff(1.0/N_BITS), + blocks.multiply_const_ff(1.0 / N_BITS), self) self.connect((self, 1), (comp, 1)) @@ -91,7 +95,7 @@ class BERAWGNSimu(gr.top_block): gr.top_block.__init__(self) self.const = digital.qpsk_constellation() # Source is N_BITS bits, non-repeated - data = map(int, numpy.random.randint(0, self.const.arity(), N_BITS/self.const.bits_per_symbol())) + data = list(map(int, numpy.random.randint(0, self.const.arity(), N_BITS / self.const.bits_per_symbol()))) src = blocks.vector_source_b(data, False) mod = digital.chunks_to_symbols_bc((self.const.points()), 1) add = blocks.add_vcc() @@ -107,12 +111,12 @@ class BERAWGNSimu(gr.top_block): def EbN0_to_noise_voltage(self, EbN0): """ Converts Eb/N0 to a complex noise voltage (assuming unit symbol power) """ - return 1.0 / math.sqrt(self.const.bits_per_symbol() * 10**(float(EbN0)/10)) + return 1.0 / math.sqrt(self.const.bits_per_symbol( * 10**(float(EbN0) / 10))) def simulate_ber(EbN0): """ All the work's done here: create flow graph, run, read out BER """ - print "Eb/N0 = %d dB" % EbN0 + print("Eb/N0 = %d dB" % EbN0) fg = BERAWGNSimu(EbN0) fg.run() return numpy.sum(fg.sink.data()) @@ -120,9 +124,9 @@ def simulate_ber(EbN0): if __name__ == "__main__": EbN0_min = 0 EbN0_max = 15 - EbN0_range = range(EbN0_min, EbN0_max+1) + EbN0_range = list(range(EbN0_min, EbN0_max+1)) ber_theory = [berawgn(x) for x in EbN0_range] - print "Simulating..." + print("Simulating...") ber_simu = [simulate_ber(x) for x in EbN0_range] f = pylab.figure() @@ -135,4 +139,3 @@ if __name__ == "__main__": s.legend() s.grid() pylab.show() - |