class fft_sink_f(gr.hier_block2, fft_sink_base):
- def __init__(self, parent, baseband_freq=0, ref_scale=1.0,
+ def __init__(self, parent, baseband_freq=0, ref_scale=2.0,
y_per_div=10, y_divs=8, ref_level=50, sample_rate=1, fft_size=512,
fft_rate=default_fft_rate, average=False, avg_alpha=None,
title='', size=default_fftsink_size, peak_hold=False):
self.log = gr.nlog10_ff(20, self.fft_size,
-20*math.log10(self.fft_size) # Adjust for number of bins
-10*math.log10(power/self.fft_size) # Adjust for windowing loss
- -20*math.log10(ref_scale)) # Adjust for reference scale
+ -20*math.log10(ref_scale/2)) # Adjust for reference scale
self.sink = gr.message_sink(gr.sizeof_float * self.fft_size, self.msgq, True)
self.connect(self, self.s2p, self.one_in_n, self.fft, self.c2mag, self.avg, self.log, self.sink)
class fft_sink_c(gr.hier_block2, fft_sink_base):
- def __init__(self, parent, baseband_freq=0, ref_scale=1.0,
+ def __init__(self, parent, baseband_freq=0, ref_scale=2.0,
y_per_div=10, y_divs=8, ref_level=50, sample_rate=1, fft_size=512,
fft_rate=default_fft_rate, average=False, avg_alpha=None,
title='', size=default_fftsink_size, peak_hold=False):
self.log = gr.nlog10_ff(20, self.fft_size,
-20*math.log10(self.fft_size) # Adjust for number of bins
-10*math.log10(power/self.fft_size) # Adjust for windowing loss
- -20*math.log10(ref_scale)) # Adjust for reference scale
+ -20*math.log10(ref_scale/2)) # Adjust for reference scale
self.sink = gr.message_sink(gr.sizeof_float * self.fft_size, self.msgq, True)
self.connect(self, self.s2p, self.one_in_n, self.fft, self.c2mag, self.avg, self.log, self.sink)