o.k, I find compiling gnuradio and uhd from source not that easy: I keep getting compiler-error-messages and messages about missing dependencies in the process. I will try to upgrage my Ubuntu. In the meantime I ran across and OOT-module named vave, which does what I want. I tried to modify it to take complex vectors, but upon execution in grc I get the error message:
ValueError: itemsize mismatch: multiply_conjugate_cc0:0 using 8192, vave_cc0:0
using 16384
The original code with float64 in place of complex works fine.
Something is wrong with the size of the interface, but I can't figure out what
it might be 1024xcomplex
should be 1024*2*32/8=8192 and not 16384 ???
Many thanks for your hints,
Wolfgang
Here is what the top-python looks like
self.connect((self.blocks_multiply_conjugate_cc_0, 0),
(self.test_blocks_vave_cc_0, 0))
...
self.blocks_multiply_conjugate_cc_0 = blocks.multiply_conjugate_cc(1024)
...
self.connect((self.blocks_multiply_conjugate_cc_0,
0)(self.test_blocks_vave_cc_0, 0))
This is vave_cc.py:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2018 <+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 copy
from gnuradio import gr
class vave_cc(gr.decim_block):
"""
Vector Average with Decimation. Only one vector is returned for N input.
This block is intended to reduce the downstream CPU load.
"""
def __init__(self, vlen, vdecimate):
gr.decim_block.__init__(self,
name="vave_cc",
in_sig=[(numpy.complex, int(vlen))],
out_sig=[(numpy.complex, int(vlen))],
decim=int(vdecimate))
self.vlen = int(vlen)
self.vdecimate = int(vdecimate)
self.sum = numpy.zeros(self.vlen)
self.count = 0
self.oneovern = 1./complex(self.vdecimate)
def forecast( self, noutput_items, ninput_items):
if noutput_items == None:
return self.vdecimate
# print 'Forecast: ', noutput_items
for i in range(len(nout_items)):
ninput_items[i] = noutput_items[i]*self.vdecimate
# print 'Forecast: ', ninput_items
return ninput_items
# return self.vdecimate
def work(self, input_items, output_items):
"""
Work averages all input vectors and outputs one vector for all inputs
"""
inn = input_items[0]
# get the number of input vectors
n = len( input_items) # number of input PORTS (only 1)
nv = len(inn) # number of vectors in this port
nout = len( output_items) #number of putput ports
ini = inn[0] # first input vector
li = len(ini) # length of first input vector
# print 'Number work vectors: ', nv, ' Length: ',li
ncp = min( li, self.vlen)
noutports = len( output_items)
if noutports != 1:
print '!!!!!!! Unexpected number of output ports: ', noutports
out = output_items[0] # vectors in PORT 0
nout = len(out) # number of output vectors
out0 = out[0] # get the first output vector
lo = len(out0) # length of 1st output vector
# print 'Number work outputs: ', nout,' Length: ',lo
iout = 0 # count the number of output vectors
for i in range(nv):
# get the lenght of one input
ini = inn[i]
ncp = min( li, self.vlen)
# now save this vector until all are received
self.sum[0:ncp] = self.sum[0:ncp] + ini[0:ncp]
self.count = self.count + 1
# indicate consumption of a vector from input
if self.count >= self.vdecimate: # normalize output
average
self.sum =
self.oneovern * self.sum# out0[:] = copy.deepcopy(self.sum)
outi = out[iout] outi =
self.sum out[iout] = outi iout = iout+1
# now reset the count and restart the sum
self.count = 0 self.sum = numpy.zeros( self.vlen)#
self.produce(0,len(output_items[0]))
#
self.consume_each(nv)
#
return no
# end for
all input vectors
# if here,
then not enough vectors input to produce an output
#
self.consume(0,nv)
output_items[0] = out# print 'N outputs: ', len(output_items[0]), iout
self.sum = self.oneovern * self.sum
# out0[:] = copy.deepcopy(self.sum)
outi = out[iout]
outi = self.sum
out[iout] = outi
iout = iout+1
# now reset the count and restart the sum
self.count = 0
self.sum = numpy.zeros( self.vlen)
# self.produce(0,len(output_items[0]))
# self.consume_each(nv)
# return no
# end for all input vectors
# if here, then not enough vectors input to produce an output
# self.consume(0,nv)
output_items[0] = out
# print 'N outputs: ', len(output_items[0]), iout
return len(output_items[0])
# end vave_cc()
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