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)
