diff options
| -rw-r--r-- | gnuradio-core/src/python/gnuradio/blks2impl/pfb_arb_resampler.py | 11 | ||||
| -rwxr-xr-x | gnuradio-examples/python/pfb/resampler.py | 95 |
2 files changed, 104 insertions, 2 deletions
diff --git a/gnuradio-core/src/python/gnuradio/blks2impl/pfb_arb_resampler.py b/gnuradio-core/src/python/gnuradio/blks2impl/pfb_arb_resampler.py index e40d9636a..cd9289fa5 100644 --- a/gnuradio-core/src/python/gnuradio/blks2impl/pfb_arb_resampler.py +++ b/gnuradio-core/src/python/gnuradio/blks2impl/pfb_arb_resampler.py @@ -31,15 +31,22 @@ class pfb_arb_resampler_ccf(gr.hier_block2): streams. This block is provided to be consistent with the interface to the other PFB block. ''' - def __init__(self, rate, taps, flt_size=32): + def __init__(self, rate, taps=None, flt_size=32, atten=80): gr.hier_block2.__init__(self, "pfb_arb_resampler_ccf", gr.io_signature(1, 1, gr.sizeof_gr_complex), # Input signature gr.io_signature(1, 1, gr.sizeof_gr_complex)) # Output signature self._rate = rate - self._taps = taps self._size = flt_size + if taps is not None: + self._taps = taps + else: + # Create a filter that covers the full bandwidth of the input signal + bw = 0.5 + tb = 0.1 + self._taps = gr.firdes.low_pass_2(self._size, self._size, bw, tb, atten) + self.pfb = gr.pfb_arb_resampler_ccf(self._rate, self._taps, self._size) self.connect(self, self.pfb) diff --git a/gnuradio-examples/python/pfb/resampler.py b/gnuradio-examples/python/pfb/resampler.py new file mode 100755 index 000000000..6be7cf14e --- /dev/null +++ b/gnuradio-examples/python/pfb/resampler.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python + +from gnuradio import gr, blks2 +import scipy, pylab + +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 = gr.firdes.low_pass_2(32, 32, 0.25, 0.1, 80) + + self.src = gr.sig_source_c(fs_in, gr.GR_SIN_WAVE, fc, 1) + #self.src = gr.noise_source_c(gr.GR_GAUSSIAN, 1) + self.head = gr.head(gr.sizeof_gr_complex, N) + + # A resampler with our taps + self.resamp_0 = blks2.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 = blks2.pfb_arb_resampler_ccf(rerate) + + self.snk_in = gr.vector_sink_c() + self.snk_0 = gr.vector_sink_c() + self.snk_1 = gr.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 = pylab.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 = scipy.arange(0, len(tb.snk_in.data())*Ts_in, Ts_in) + t_out = scipy.arange(0, len(tb.snk_0.data())*Ts_out, Ts_out) + + fig2 = pylab.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() + + pylab.show() + +if __name__ == "__main__": + main() + |