Wideband Spectrum Analyzer


#1

Hi everybody!

I have modified usrp_spectrum_sense.py to plot the results with gnuplot.
There are two files: widespectrum.py and plot.p
I would like everybody to test it and report me the errors and how can I
improve it.
I’ve used USRPv1 + Flex2400.

Thanks in advance!

Here it goes…

WIDESPECTRUM.PY:

#!/usr/bin/env python

Copyright 2005,2007 Free Software Foundation, Inc.

This file is part of GNU Radio

GNU Radio 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.

GNU Radio 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 GNU Radio; see the file COPYING. If not, write to

the Free Software Foundation, Inc., 51 Franklin Street,

Boston, MA 02110-1301, USA.

from gnuradio import gr, gru, eng_notation, optfir, window
from gnuradio import audio
from gnuradio import usrp
from gnuradio.eng_option import eng_option
from optparse import OptionParser
from usrpm import usrp_dbid
import sys
import math
import struct
import Gnuplot, Gnuplot.funcutils # Added to view the results

class tune(gr.feval_dd):
“”"
This class allows C++ code to callback into python.
“”"
def init(self, tb):
gr.feval_dd.init(self)
self.tb = tb

def eval(self, ignore):
    """
    This method is called from gr.bin_statistics_f when it wants to

change
the center frequency. This method tunes the front end to the
new
center
frequency, and returns the new frequency as its result.
“”"
try:
# We use this try block so that if something goes wrong from
here
# down, at least we’ll have a prayer of knowing what went
wrong.
# Without this, you get a very mysterious:
#
# terminate called after throwing an instance of
‘Swig::DirectorMethodException’
# Aborted
#
# message on stderr. Not exactly helpful :wink:

        new_freq = self.tb.set_next_freq()
        return new_freq

    except Exception, e:
        print "tune: Exception: ", e

class parse_msg(object):
def init(self, msg):
self.center_freq = msg.arg1()
self.vlen = int(msg.arg2())
assert(msg.length() == self.vlen * gr.sizeof_float)

    # FIXME consider using Numarray or NumPy vector
    t = msg.to_string()
    self.raw_data = t
    self.data = struct.unpack('%df' % (self.vlen,), t)

class my_top_block(gr.top_block):

def __init__(self):
    gr.top_block.__init__(self)

    usage = "usage: %prog [options] min_freq max_freq"
# Example:  ./widespectrum.py 2.23G 2.93G
# that is the maximun range of the USRP Flex2400 device.

parser = OptionParser(option_class=eng_option, usage=usage)
    parser.add_option("-R", "--rx-subdev-spec", type="subdev",

default=(0,0),
help=“select USRP Rx side A or B (default=A)”)
parser.add_option("-g", “–gain”, type=“eng_float”,
default=None,
help=“set gain in dB (default is midpoint)”)
parser.add_option("", “–tune-delay”, type=“eng_float”,
default=1e-3, metavar=“SECS”,
help=“time to delay (in seconds) after
changing
frequency [default=%default]”)
parser.add_option("", “–dwell-delay”, type=“eng_float”,
default=10e-3, metavar=“SECS”,
help=“time to dwell (in seconds) at a given
frequncy [default=%default]”)
parser.add_option("-F", “–fft-size”, type=“int”, default=256,
help=“specify number of FFT bins
[default=%default]”)
parser.add_option("-d", “–decim”, type=“intx”, default=64,
help=“set decimation to DECIM
[default=%default]”)
parser.add_option("", “–real-time”, action=“store_true”,
default=False,
help=“Attempt to enable real-time scheduling”)
parser.add_option("-B", “–fusb-block-size”, type=“int”,
default=0,
help=“specify fast usb block size
[default=%default]”)
parser.add_option("-N", “–fusb-nblocks”, type=“int”, default=0,
help=“specify number of fast usb blocks
[default=%default]”)

    (options, args) = parser.parse_args()
    if len(args) != 2:
        parser.print_help()
        sys.exit(1)

    self.min_freq = eng_notation.str_to_num(args[0])
    self.max_freq = eng_notation.str_to_num(args[1])

    if self.min_freq > self.max_freq:
        self.min_freq, self.max_freq = self.max_freq, self.min_freq 

swap them

# FIXME We set MANUALLY the physical limits of the device. In this 

case
the USRP Flex2400 limits.

if self.min_freq < 2222000000:
    print ("The minimum frequency of this device is 2.222GHz")
    self.min_freq = 2222000000

if self.max_freq <  2222000000:
    print ("The minimum frequency of this device is 2.222GHz")
    self.max_freq = 2222000000

if self.min_freq > 2937000000:
    print ("The maximun frequency of this device is 2.937GHz")
    self.min_freq = 2937000000

if self.max_freq > 2937000000:
    print ("The maximun frequency of this device is 2.937GHz")
    self.max_freq = 2937000000

if self.min_freq == self.max_freq:
    print ("Do not use this program for a single frecuency analysis

please")
exit()

self.fft_size = options.fft_size


    if not options.real_time:
        realtime = False
    else:
        # Attempt to enable realtime scheduling
        r = gr.enable_realtime_scheduling()
        if r == gr.RT_OK:
            realtime = True
        else:
            realtime = False
            print "Note: failed to enable realtime scheduling"

    # If the user hasn't set the fusb_* parameters on the command 

line,
# pick some values that will reduce latency.

    if 1:
        if options.fusb_block_size == 0 and options.fusb_nblocks == 

0:
if realtime: # be more aggressive
options.fusb_block_size =
gr.prefs().get_long(‘fusb’,
‘rt_block_size’, 1024)
options.fusb_nblocks =
gr.prefs().get_long(‘fusb’,
‘rt_nblocks’, 16)
else:
options.fusb_block_size =
gr.prefs().get_long(‘fusb’,
‘block_size’, 4096)
options.fusb_nblocks =
gr.prefs().get_long(‘fusb’,
‘nblocks’, 16)

    #print "fusb_block_size =", options.fusb_block_size
#print "fusb_nblocks    =", options.fusb_nblocks

    # build graph

    self.u = usrp.source_c(fusb_block_size=options.fusb_block_size,
                           fusb_nblocks=options.fusb_nblocks)


    adc_rate = self.u.adc_rate()                # 64 MS/s
    usrp_decim = options.decim
    self.u.set_decim_rate(usrp_decim)
    usrp_rate = adc_rate / usrp_decim

    self.u.set_mux(usrp.determine_rx_mux_value(self.u,

options.rx_subdev_spec))
self.subdev = usrp.selected_subdev(self.u,
options.rx_subdev_spec)
print “Using RX d’board %s” % (self.subdev.side_and_name(),)

s2v = gr.stream_to_vector(gr.sizeof_gr_complex, self.fft_size)

    mywindow = window.blackmanharris(self.fft_size)
    fft = gr.fft_vcc(self.fft_size, True, mywindow)
    power = 0
    for tap in mywindow:
        power += tap*tap

    c2mag = gr.complex_to_mag_squared(self.fft_size)

    # FIXME the log10 primitive is dog slow
    log = gr.nlog10_ff(10, self.fft_size,

-20math.log10(self.fft_size)-10math.log10(power/self.fft_size))

    # Set the freq_step to 75% of the actual data throughput.
    # This allows us to discard the bins on both ends of the 

spectrum.

self.freq_step = 0.75 * usrp_rate
    self.min_center_freq = self.min_freq + self.freq_step/2
    nsteps = math.ceil((self.max_freq - self.min_freq) / 

self.freq_step)
self.max_center_freq = self.min_center_freq + (nsteps *
self.freq_step)

    self.next_freq = self.min_center_freq

# We define the minimum, maximum and frequency step in a global

statement to use them later.

global min_center_freq, max_center_freq, freq_step
min_center_freq = self.min_center_freq
max_center_freq = self.max_center_freq
freq_step = self.freq_step


    tune_delay  = max(0, int(round(options.tune_delay * usrp_rate /

self.fft_size))) # in fft_frames
dwell_delay = max(1, int(round(options.dwell_delay * usrp_rate /
self.fft_size))) # in fft_frames

    self.msgq = gr.msg_queue(16)
    self._tune_callback = tune(self)        # hang on to this to 

keep it
from being GC’d
stats = gr.bin_statistics_f(self.fft_size, self.msgq,
self._tune_callback, tune_delay,
dwell_delay)

    # FIXME leave out the log10 until we speed it up
self.connect(self.u, s2v, fft, c2mag, log, stats)
#self.connect(self.u, s2v, fft, c2mag, stats)

    if options.gain is None:
        # if no gain was specified, use the mid-point in dB
        g = self.subdev.gain_range()
        options.gain = float(g[0]+g[1])/2

    self.set_gain(options.gain)
print "gain =", options.gain


def set_next_freq(self):
    target_freq = self.next_freq
    self.next_freq = self.next_freq + self.freq_step
    if self.next_freq >= self.max_center_freq:
        self.next_freq = self.min_center_freq

    if not self.set_freq(target_freq):
        print "Failed to set frequency to", target_freq

    return target_freq


def set_freq(self, target_freq):
    """
    Set the center frequency we're interested in.

    @param target_freq: frequency in Hz
    @rypte: bool

    Tuning is a two step process.  First we ask the front-end to
    tune as close to the desired frequency as it can.  Then we use
    the result of that operation and our target_frequency to
    determine the value for the digital down converter.
    """
    return self.u.tune(0, self.subdev, target_freq)


def set_gain(self, gain):
    self.subdev.set_gain(gain)

def mean(data): # Returns the arithmetic mean of a
numeric
list
return sum(data) / len(data)

def main_loop(tb):

# We give basic information about the Spectrum Analysis

print "The start frequency is %s Hz" % min_center_freq
print "The final frequency is %s Hz" % max_center_freq
print "The frequency step is %s Hz" % freq_step
g = Gnuplot.Gnuplot(debug=1)

while 1:

    # Get the next message sent from the C++ code (blocking call).
    # It contains the center frequency and the mag squared of the 

fft
m = parse_msg(tb.msgq.delete_head())

    # Print center freq so we know that something is happening...
    #print (m.center_freq)

# FIXME do something useful with the data...


# Mechanism to save in a file (power.dat) 2 columns, one for the

frequencies and the other for the mean of the FFT_SIZE points of m.data

if m.center_freq == min_center_freq:    # If we get the minimum

frequency, it’ll reset the power.dat file
power=open(“power.dat”, “w”) # It will overwrite the
power.dat
file

power=open("power.dat", "a")        # Each loop, it adds a dataline

(append)
p=str(m.center_freq) # with a frequency and the mean of
the
256 FFT samples (Power in dB)
media=str(mean(m.data)) #
todo= p + " " + media + ‘\n’ #
power.write(todo) #

if m.center_freq == (max_center_freq-freq_step):    # If it gets the

final frecuency

    p=str(m.center_freq)                # It'll write the last 

frecuency
with its Power in the power.dat file
media=str(mean(m.data)) #
todo= p + " " + media + ‘\n’ #
power.write(todo) #
g.load(“plot.p”) # Load the plot with the data
obtained from URSP
power=open(“power.dat”, “a”) # Without this line, the
file will start with the last frecuency
#g.hardcopy(‘spectrum.ps’, enhanced=1, color=1) # It does
a
plot copy to the hard disk (I think there’s not enough time to do it)

# m.data in 'w' mode: only write, if it exist a file with the same 

name,
it’ll be overwrite.
# ‘a’ to append
# ‘r+’ for read and write

    # m.data are the mag_squared of the fft output (they are in the
    # standard order.  I.e., bin 0 == DC.)
    # You'll probably want to do the equivalent of "fftshift" on 

them

# m.raw_data is a string that contains the binary floats.
    # You could write this as binary to a file.

if name == ‘main’:
tb = my_top_block()
try:
tb.start() # start executing flow graph in another
thread…
main_loop(tb)

except KeyboardInterrupt:
    pass

PLOT.P*

set autoscale
unset logscale
unset label
set xtic auto
set ytic auto
set title “Wideband Spectrum Analyzer”
set xlabel “Frecuency”
set ylabel “Power (dB)”
set grid
plot “power.dat” using 1:2 title ‘Mean power’ with linespoints


#2

Hello Santi,

I am currently working on trying to implement a wideband spectrum
analyzer as well, both for real-time and offline analysis.

The main problem I am facing right now is what should I do with the
output of usrp_spectrum_sense.

In your implementation above you have used the average of the values in
the m.data vector. From the documentation in the code, this is the
average of the magnitude of the fft values squared.

In another post however, http://www.ruby-forum.com/topic/174437 : Some
usrp_spectrum_sense.py code explanation, the user suggested that to get
power, take the square root of the output. (each value?S Sum of values?)

And in yet another post on another forum, the user used:
for bin in m.data:
signalPower += bin
signalPower = 10math.log10(signalPower) -
10
math.log10(tb.fft_size) - 20*math.log10(tb.power) - tb.gain

I am now lost as to what values I should really be displaying, and what
are the appropriate units on the axis.

Please help if you can.

Yohan