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#!/usr/bin/env python
#
# Copyright 2009,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from gnuradio import gr
from gnuradio import filter
from gnuradio import blocks
import sys
import numpy
try:
from gnuradio import analog
except ImportError:
sys.stderr.write("Error: Program requires gr-analog.\n")
sys.exit(1)
try:
from matplotlib import pyplot
except ImportError:
sys.stderr.write("Error: Program requires matplotlib (see: matplotlib.sourceforge.net).\n")
sys.exit(1)
class mytb(gr.top_block):
def __init__(self, fs_in, fs_out, fc, N=10000):
gr.top_block.__init__(self)
rerate = float(fs_out) / float(fs_in)
print("Resampling from %f to %f by %f " %(fs_in, fs_out, rerate))
# Creating our own taps
taps = filter.firdes.low_pass_2(32, 32, 0.25, 0.1, 80)
self.src = analog.sig_source_c(fs_in, analog.GR_SIN_WAVE, fc, 1)
#self.src = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
self.head = blocks.head(gr.sizeof_gr_complex, N)
# A resampler with our taps
self.resamp_0 = filter.pfb.arb_resampler_ccf(rerate, taps,
flt_size=32)
# A resampler that just needs a resampling rate.
# Filter is created for us and designed to cover
# entire bandwidth of the input signal.
# An optional atten=XX rate can be used here to
# specify the out-of-band rejection (default=80).
self.resamp_1 = filter.pfb.arb_resampler_ccf(rerate)
self.snk_in = blocks.vector_sink_c()
self.snk_0 = blocks.vector_sink_c()
self.snk_1 = blocks.vector_sink_c()
self.connect(self.src, self.head, self.snk_in)
self.connect(self.head, self.resamp_0, self.snk_0)
self.connect(self.head, self.resamp_1, self.snk_1)
def main():
fs_in = 8000
fs_out = 20000
fc = 1000
N = 10000
tb = mytb(fs_in, fs_out, fc, N)
tb.run()
# Plot PSD of signals
nfftsize = 2048
fig1 = pyplot.figure(1, figsize=(10,10), facecolor="w")
sp1 = fig1.add_subplot(2,1,1)
sp1.psd(tb.snk_in.data(), NFFT=nfftsize,
noverlap=nfftsize / 4, Fs = fs_in)
sp1.set_title(("Input Signal at f_s=%.2f kHz" % (fs_in / 1000.0)))
sp1.set_xlim([-fs_in / 2, fs_in / 2])
sp2 = fig1.add_subplot(2,1,2)
sp2.psd(tb.snk_0.data(), NFFT=nfftsize,
noverlap=nfftsize / 4, Fs = fs_out,
label="With our filter")
sp2.psd(tb.snk_1.data(), NFFT=nfftsize,
noverlap=nfftsize / 4, Fs = fs_out,
label="With auto-generated filter")
sp2.set_title(("Output Signals at f_s=%.2f kHz" % (fs_out / 1000.0)))
sp2.set_xlim([-fs_out / 2, fs_out / 2])
sp2.legend()
# Plot signals in time
Ts_in = 1.0 / fs_in
Ts_out = 1.0 / fs_out
t_in = numpy.arange(0, len(tb.snk_in.data())*Ts_in, Ts_in)
t_out = numpy.arange(0, len(tb.snk_0.data())*Ts_out, Ts_out)
fig2 = pyplot.figure(2, figsize=(10,10), facecolor="w")
sp21 = fig2.add_subplot(2,1,1)
sp21.plot(t_in, tb.snk_in.data())
sp21.set_title(("Input Signal at f_s=%.2f kHz" % (fs_in / 1000.0)))
sp21.set_xlim([t_in[100], t_in[200]])
sp22 = fig2.add_subplot(2,1,2)
sp22.plot(t_out, tb.snk_0.data(),
label="With our filter")
sp22.plot(t_out, tb.snk_1.data(),
label="With auto-generated filter")
sp22.set_title(("Output Signals at f_s=%.2f kHz" % (fs_out / 1000.0)))
r = float(fs_out) / float(fs_in)
sp22.set_xlim([t_out[r * 100], t_out[r * 200]])
sp22.legend()
pyplot.show()
if __name__ == "__main__":
main()
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