def signal_through_ma_channel(sig_tap, noise_tap, i): signal = np.load("/home/creasy/workplace/data/exp_deviate_one_%d.npy"%(i))[:210000] receive = ir.moving_average(sig_tap, signal) # save signal snr=+inf np.save("temp/ma_data_%d.npy"%(i), receive) # snr = 0..20 (scale from 1 to 100) for j in range(-10,21): amp = 10**(j/10.) white = np.random.normal(0, sqrt(sum(sig_tap)**2/amp), len(signal)) color_scale = sqrt(sum(sig_tap)**2/amp/(sum(noise_tap)**2)) color = ir.moving_average(noise_tap, np.random.normal(0, color_scale, len(signal))) np.save("temp/ma_data_white_%d_%d.npy"%(j, i), white+receive) np.save("temp/ma_data_color_%d_%d.npy"%(j, i), color+receive)
def signal_through_ma_channel(sig_tap, noise_tap, i): signal = np.load("/home/creasy/workplace/data/exp_deviate_one_%d.npy" % (i))[:210000] receive = ir.moving_average(sig_tap, signal) # save signal snr=+inf np.save("temp/ma_data_%d.npy" % (i), receive) # snr = 0..20 (scale from 1 to 100) for j in range(-10, 21): amp = 10**(j / 10.) white = np.random.normal(0, sqrt(sum(sig_tap)**2 / amp), len(signal)) color_scale = sqrt(sum(sig_tap)**2 / amp / (sum(noise_tap)**2)) color = ir.moving_average( noise_tap, np.random.normal(0, color_scale, len(signal))) np.save("temp/ma_data_white_%d_%d.npy" % (j, i), white + receive) np.save("temp/ma_data_color_%d_%d.npy" % (j, i), color + receive)
def task(pcs, taps, winsize, r): if len(taps) <= 3: file_tag = "short" else: file_tag = "long" f = open("pcs_montecarlo_%s_ma%d_%d_%d.csv"%(file_tag, len(pcs), winsize, int(''.join(map(str,pcs)))), 'w') for i in range(r): signal = np.load("/home/work/data/exp_deviate_one_%d.npy"%(i)) receive = ir.moving_average(taps, signal) temp = ma.maestx (receive, pcs, len(taps)-1, len(pcs), winsize) f.write('%s\n' % temp) print temp f.close()
def task(pcs, taps, winsize, r, slicing): if len(taps) <= 3: file_tag = "short" else: file_tag = "long" f = open("ma_test_%s_hos%d_%d_slice%d_%d.csv"%(file_tag, len(pcs), winsize, slicing, int(''.join(map(str,pcs)))), 'w') for i in range(r): signal = np.load("/home/work/rsls/data/exp_deviate_one_%d.npy"%(i))[:slicing] receive = ir.moving_average(taps, signal) temp = ma.maestx (receive, len(taps)-1, len(pcs), winsize) f.write('%s\n' % temp) print temp f.close()
def task_nma(pcs, taps, winsize, r, slicing): if len(taps) <= 3: file_tag = "short" else: file_tag = "long" f = open("../result/mns_montecarlo_%s_ma%d_%d_slice%d_%d.csv"%(file_tag, len(pcs), winsize, slicing, int(''.join(map(str,pcs)))), 'w') for i in range(r): signal = np.load("../data/exp_deviate_one_%d.npy"%(i))[:slicing] receive = ir.moving_average(taps, signal) temp = nma.maestx (receive, pcs, len(taps)-1, len(pcs), winsize) f.write('%s\n' % temp) print temp f.close()
def task_cx(pcs, taps, winsize, r, slicing): if len(taps) <= 3: file_tag = "short" else: file_tag = "long" f = open( "../result/pcs_montecarlo_%s_cx%d_%d_%d_slice%d.csv" % (file_tag, len(pcs), winsize, int(''.join(map(str, pcs))), slicing), 'w') for i in range(r): signal = np.load("../data/exp_deviate_one_%d.npy" % (i))[:slicing] receive = ir.moving_average(taps, signal) temp = cx.cumx(receive, pcs, len(pcs), len(taps) - 1, winsize) f.write('%s\n' % temp) print temp f.close()
# for the convinence of simulation import numpy as np import impulse_response as ir import maest as ma x = np.load("../data/exp_deviate_one_0.npy") y = ir.moving_average(2, [1, -2.333, 0.667], x)
import cumxst as cx import nested_cumxst as ncx import numpy as np import nested_maest as nma import maest as ma import impulse_response as ir winsize = 512 taps = [1, -2.333, 0.667] signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) nl = [4, 3, 4] print "With nested sampling:", ncx.cumx(receive, nl, len(nl), len(taps) - 1, winsize) signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) pcs = [2, 3, 5] print "Without downsampling:", cx.cumx(receive, pcs, len(pcs), len(taps) - 1, winsize) signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) pcs = [1, 1, 1] print "With PCS downsampling:", cx.cumx(receive, pcs, len(pcs), len(taps) - 1, winsize) ####################### signal = np.load("../data/exp_deviate_one_0.npy")[:10000]
import cumxst as cx import nested_cumxst as ncx import numpy as np import nested_maest as nma import maest as ma import impulse_response as ir winsize = 512 taps = [1, -2.333, 0.667] signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) nl = [4,3,4] print "With nested sampling:", ncx.cumx(receive, nl, len(nl), len(taps)-1, winsize) signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) pcs = [2,3,5] print "Without downsampling:", cx.cumx(receive, pcs, len(pcs), len(taps)-1, winsize) signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) pcs = [1,1,1] print "With PCS downsampling:", cx.cumx(receive, pcs, len(pcs), len(taps)-1, winsize) ####################### signal = np.load("../data/exp_deviate_one_0.npy")[:10000] receive = ir.moving_average(taps, signal) pcs = [4,3,4] print "With nested sampling:", nma.maestx (receive, pcs, len(taps)-1, len(pcs), winsize)