diff options
Diffstat (limited to 'gr-digital/examples/snr_estimators.py')
| -rw-r--r--[-rwxr-xr-x] | gr-digital/examples/snr_estimators.py | 36 |
1 files changed, 20 insertions, 16 deletions
diff --git a/gr-digital/examples/snr_estimators.py b/gr-digital/examples/snr_estimators.py index 31efe6b83e..b1decd84d8 100755..100644 --- a/gr-digital/examples/snr_estimators.py +++ b/gr-digital/examples/snr_estimators.py @@ -20,19 +20,23 @@ # Boston, MA 02110-1301, USA. # +from __future__ import print_function +from __future__ import division +from __future__ import unicode_literals + import sys try: import scipy from scipy import stats except ImportError: - print "Error: Program requires scipy (www.scipy.org)." + print("Error: Program requires scipy (www.scipy.org).") sys.exit(1) try: import pylab except ImportError: - print "Error: Program requires Matplotlib (matplotlib.sourceforge.net)." + print("Error: Program requires Matplotlib (matplotlib.sourceforge.net).") sys.exit(1) from gnuradio import gr, digital, filter @@ -55,7 +59,7 @@ def online_skewness(data): M2 = 0 M3 = 0 - for n in xrange(len(data)): + for n in range(len(data)): delta = data[n] - mean delta_n = delta / (n+1) term1 = delta * delta_n * n @@ -63,12 +67,12 @@ def online_skewness(data): M3 = M3 + term1 * delta_n * (n - 1) - 3 * delta_n * M2 M2 = M2 + term1 - return scipy.sqrt(len(data))*M3 / scipy.power(M2, 3.0/2.0); + return scipy.sqrt(len(data))*M3 / scipy.power(M2, 3.0 / 2.0); def snr_est_simple(signal): s = scipy.mean(abs(signal)**2) n = 2*scipy.var(abs(signal)) - snr_rat = s/n + snr_rat = s / n return 10.0*scipy.log10(snr_rat), snr_rat def snr_est_skew(signal): @@ -94,11 +98,11 @@ def snr_est_svr(signal): N = len(signal) ssum = 0 msum = 0 - for i in xrange(1, N): + for i in range(1, N): ssum += (abs(signal[i])**2)*(abs(signal[i-1])**2) msum += (abs(signal[i])**4) - savg = (1.0/(float(N)-1.0))*ssum - mavg = (1.0/(float(N)-1.0))*msum + savg = (1.0 / (float(N-1.0)))*ssum + mavg = (1.0 / (float(N-1.0)))*msum beta = savg / (mavg - savg) snr_rat = ((beta - 1) + scipy.sqrt(beta*(beta-1))) @@ -126,9 +130,9 @@ def main(): parser.add_option("", "--snr-step", type="float", default=0.5, help="SNR step amount [default=%default]") parser.add_option("-t", "--type", type="choice", - choices=gr_estimators.keys(), default="simple", + choices=list(gr_estimators.keys()), default="simple", help="Estimator type {0} [default=%default]".format( - gr_estimators.keys())) + list(gr_estimators.keys()))) (options, args) = parser.parse_args () N = options.nsamples @@ -155,14 +159,14 @@ def main(): SNR_step = options.snr_step SNR_dB = scipy.arange(SNR_min, SNR_max+SNR_step, SNR_step) for snr in SNR_dB: - SNR = 10.0**(snr/10.0) + SNR = 10.0**(snr / 10.0) scale = scipy.sqrt(2*SNR) - yy = bits + n_cpx/scale - print "SNR: ", snr + yy = bits + n_cpx / scale + print("SNR: ", snr) Sknown = scipy.mean(yy**2) - Nknown = scipy.var(n_cpx/scale) - snr0 = Sknown/Nknown + Nknown = scipy.var(n_cpx / scale) + snr0 = Sknown / Nknown snr0dB = 10.0*scipy.log10(snr0) snr_known.append(float(snr0dB)) @@ -171,7 +175,7 @@ def main(): gr_src = blocks.vector_source_c(bits.tolist(), False) gr_snr = digital.mpsk_snr_est_cc(gr_est, ntag, 0.001) - gr_chn = channels.channel_model(1.0/scale) + gr_chn = channels.channel_model(1.0 / scale) gr_snk = blocks.null_sink(gr.sizeof_gr_complex) tb = gr.top_block() tb.connect(gr_src, gr_chn, gr_snr, gr_snk) |