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| author | Tom Rondeau <trondeau@vt.edu> | 2012-04-13 18:36:53 -0400 |
|---|---|---|
| committer | Tom Rondeau <trondeau@vt.edu> | 2012-04-13 18:36:53 -0400 |
| commit | f919f9dcbb54a08e6e26d6c229ce92fb784fa1b2 (patch) | |
| tree | 7e846386b9eb1676f9a93fc4a1e55916b9accc97 /gr-trellis/src/examples/python/test_turbo_equalization2.py | |
| parent | 6a1e9783fec6ed827f49db27c171591d30f32933 (diff) | |
Removed whitespace and added dtools/bin/remove-whitespace as a tool to do this in the future.
The sed script was provided by Moritz Fischer.
Diffstat (limited to 'gr-trellis/src/examples/python/test_turbo_equalization2.py')
| -rwxr-xr-x | gr-trellis/src/examples/python/test_turbo_equalization2.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/gr-trellis/src/examples/python/test_turbo_equalization2.py b/gr-trellis/src/examples/python/test_turbo_equalization2.py index 7e4341cba3..5a6c77e9d4 100755 --- a/gr-trellis/src/examples/python/test_turbo_equalization2.py +++ b/gr-trellis/src/examples/python/test_turbo_equalization2.py @@ -65,17 +65,17 @@ def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,channel,modulation,dimensiona isi = gr.fir_filter_fff(1,channel) add = gr.add_ff() noise = gr.noise_source_f(gr.GR_GAUSSIAN,math.sqrt(N0/2),seed) - + # RX - (head,tail) = make_rx(tb,fo,fi,dimensionality,tot_constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_MIN_SUM) - dst = gr.vector_sink_s(); - + (head,tail) = make_rx(tb,fo,fi,dimensionality,tot_constellation,K,interleaver,IT,Es,N0,trellis.TRELLIS_MIN_SUM) + dst = gr.vector_sink_s(); + tb.connect (src,enc_out,inter,mod) tb.connect (mod,isi,(add,0)) tb.connect (noise,(add,1)) tb.connect (add,head) tb.connect (tail,dst) - + tb.run() data = dst.data() @@ -86,7 +86,7 @@ def run_test (fo,fi,interleaver,Kb,bitspersymbol,K,channel,modulation,dimensiona nright=nright+1 #else: #print "Error in ", i - + return (ntotal,ntotal-nright) @@ -117,7 +117,7 @@ def main(args): tot_channel = fsm_utils.make_isi_lookup(modulation,channel,True) # generate the lookup table (normalize energy to 1) dimensionality = tot_channel[0] N0=pow(10.0,-esn0_db/10.0); # noise variance - tot_constellation =[0]*len(tot_channel[1]) + tot_constellation =[0]*len(tot_channel[1]) for i in range(len(tot_channel[1])): tot_constellation[i] = tot_channel[1][i] * math.sqrt(1.0/N0) if len(tot_constellation)/dimensionality != fi.O(): |