Hi Al,
We’re trying to use your gr-dsp library and are having difficulty
verifying the output of your DSP.fir_ccf blocks. To allow for easy
comparison to the standard filter type, gr.fir_filter_ccf, we generated
a very simple block diagram in GRC. This consisted of a vector source,
an fir_filter_ccf block, and a file sink. All of the original data and
filter taps are the same, but the outputs are not lining up with their
expected values.
I have included the full script file at the bottom of this email. The
relevant calls to the filter constructors are shown in the text.
For instance:
We have:
src = (0.01+0.11j,
0.02+0.22j,
0.03+0.33j,
0.04+0.44j,
0.05+0.55j,
0.06+0.66j,
0.07+0.77j,
0.08+0.88j,
0.09+0.99j)
src_coeff = (0.101, 0.102, 0.103, 0.104, 0.105)
Without scaling (scaling_factor = 0, so scaling by 2^0 = 1):
gr.fir_filter_ccf(1, src_coeff)
This produces output:
0.0010 + 0.0111i
0.0030 + 0.0334i
0.0061 + 0.0671i
0.0102 + 0.1122i
0.0154 + 0.1689i
0.0205 + 0.2255i
0.0257 + 0.2822i
0.0308 + 0.3388i
0.0360 + 0.3954i
What we thought would be the equivalent call using the fir_ccf
block is:
self.gr_fir_filter_xxx_0 = dsp.fir_ccf (src_coeff, 0, 1, 0, 0, 0,
This produces output:
0
0
0
0
0
0
0
0
0
With scaling (scaling_factor = 15, so scaling by 2^15):
gr.fir_filter_ccf(1, src_coeff)
The data was manually scaled by 2^15 in MATLAB, producing output:
1.0e+04 *
0.0033 + 0.0364i
0.0100 + 0.1096i
0.0200 + 0.2199i
0.0334 + 0.3677i
0.0503 + 0.5533i
0.0672 + 0.7389i
0.0840 + 0.9245i
0.1009 + 1.1102i
0.1178 + 1.2958i
dsp.fir_ccf (src_coeff, 15, 1, 0, 1, 0, 0)
* output-signature = 1, so we want the output to be have the same
scale factor that it is on the DSP.
This produces output:
1.0e+03 *
0.3350 + 0.3350i
0.3400 + 0.3390i
0.3430 + 0.3440i
0.3470 + 0.3470i
0.3500 - 0.0340i
-0.3650 - 0.1000i
-1.0960 - 0.2000i
-2.1990 - 0.3350i
-3.6770 - 0.5030i
In neither of these cases do the dsp implementation and the gpp
implementation give the same output.
I’m pretty sure that the issue is in my interpretation of your
parameters. I’ve already been using the online documentation to figure
out what the parameters do, so I know the basic jist of it, but
obviously I haven’t got it figured out yet. Could you please explain
the use of the scaling_factor, input_signature, and output_signature
parameters in more detail?
Also, for the input_signature parameter to be 0, like it is in the
examples qa_fir_ccf2.py and qa_fir_ccf3.py, doesn’t the input need to be
normalized? By my understanding, normalized vectors are unit vectors,
so they should have length 1. But src (above) has length 9, so it’s not
normalized and the input_signature parameter should be 1. Is that
correct?
Thanks,
Chris
#!/usr/bin/env python
##################################################
Gnuradio Python Flow Graph
Title: Top Block
Generated: Wed Jul 13 11:09:34 2011
##################################################
from gnuradio import eng_notation
from gnuradio import gr
from gnuradio.eng_option import eng_option
from gnuradio.gr import firdes
from optparse import OptionParser
from gnuradio import dsp
class top_block(gr.top_block):
def __init__(self):
gr.top_block.__init__(self, "Top Block")
##################################################
# Variables
##################################################
self.samp_rate = samp_rate = 32000
##################################################
# Blocks
##################################################
self.gr_vector_source_x_0 =
gr.vector_source_c((0.01+0.11j,0.02+0.22j,0.03+0.33j,0.04+0.44j,0.05+0.55j,
0.06+0.66j, .07+0.77j, 0.08+0.88j, 0.09+0.99j), False, 1)
#self.gr_fir_filter_xxx_0 = gr.fir_filter_ccf(1,
(0.101, 0.102, 0.103, 0.104, 0.105))
# Uncomment the previous line, comment in the next
three lines to switch from dsp-based to gpp-based filter.
src_coeff = (0.101, 0.102, 0.103, 0.104, 0.105)
dsp.init()
self.gr_fir_filter_xxx_0 = dsp.fir_ccf (src_coeff, 15,
1, 0, 1, 0, 0)
self.gr_file_sink_0 =
gr.file_sink(gr.sizeof_gr_complex*1, “filtertest-dsp2.dat”)
self.gr_file_sink_0.set_unbuffered(False)
##################################################
# Connections
##################################################
self.connect((self.gr_vector_source_x_0, 0),
(self.gr_fir_filter_xxx_0, 0))
self.connect((self.gr_fir_filter_xxx_0, 0),
(self.gr_file_sink_0, 0))
def get_samp_rate(self):
return self.samp_rate
def set_samp_rate(self, samp_rate):
self.samp_rate = samp_rate
if name == ‘main’:
parser = OptionParser(option_class=eng_option, usage="%prog:
[options]")
(options, args) = parser.parse_args()
if gr.enable_realtime_scheduling() != gr.RT_OK:
print “Error: failed to enable realtime scheduling.”
tb = top_block()
tb.start()
raw_input('Press Enter to quit: ')
tb.stop()
