Hi all,
sorry I forgot to put my python script in last email.
I'm using python to build an OOT block to do Maximum-Minimum Eigenvalue
detection, and I have followed the steps of the algorithm, but when I connect
it to a signal source and number sink, it runs too slow and even no results,
and the CPU occupation will come to near 100%.
I want to know the outcome of signal source is a vector, but how many samples
comes to next blocks every time? cos I want to reshape them as arrays to
compute their covariance matrix. Are there any problems in my python script?
Thanks in advance
Yan
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2016 <+YOU OR YOUR COMPANY+>.
#
# This is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# This software is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this software; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
import numpy
import scipy.linalg
import numpy.linalg
import math
from TracyWidom import *
from gnuradio import gr
class MME(gr.sync_block):
"""
docstring for block MME
"""
def __init__(self,samples,slots,false,algo):
self.samples = samples
self.slots = slots
self.false = false
self.algo = algo
gr.sync_block.__init__(self,
name="MME",
in_sig=[numpy.float32],
out_sig=[numpy.float32])
'''
def R(self, x):
x0 = x - numpy.mean(x)
L = self.slots
Ns = len(x0)
lbd = numpy.empty(L)
for l in xrange(L):
if l > 0:
xu = x0[:-l]
else:
xu = x0
lbd[l] = numpy.dot(xu, x0[l:])/(Ns-l)
return scipy.linalg.toeplitz(lbd)
'''
#compute the eigenvalues
def lbd(self, x):
#R = self.R(x)
R = numpy.cov(x) #compute the received sample covariance matrix
lbd = numpy.linalg.eigvalsh(R)
return numpy.abs(lbd)
#compute the threshold
def thr_MME(self,Ns,M,false):
x = math.sqrt(Ns)+math.sqrt(M*Ns)
y = math.sqrt(Ns)-math.sqrt(M*Ns)
f = TracyWidom().cdfinv(1-false)
thr1 = x*x/(y*y)*(1+x**(-2/3)/(M*Ns*Ns)**(1/6)*f)
return thr1
def work(self, input_items, output_items):
in0 = input_items[0]
out = output_items[0]
Ns = self.samples #number of samples
M = self.slots #number of slots
false = self.false #probability of false alarm
# <+signal processing here+>
n = len(in0)/Ns/M
in1 = numpy.array(in0)
in1 = in1[0:n*Ns*M:1] #slice the array
in1 = numpy.reshape(in1,(n*M,Ns)) # >>> x = np.arange(16.0).reshape(4, 4)
in2 = numpy.split(in1,n)
if(self.algo == 0): #use MME detection
for i in range(n):
lbd = self.lbd(in2[i])
lbd.sort()
out[i] = lbd[-1]/lbd[0]
if (out[i] >= self.thr_MME(Ns,M,false)):
out[i] = 1
else:
out[i] = 0
if(self.algo == 1): #use EME detection
for i in range(n):
lbd = self.lbd(in2[i])
lbd.sort()
out[i] = numpy.sum(lbd**2)/lbd[0]
return len(output_items[0])
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