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jblitzar
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github-python-corpus

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for i in inputs:
print ('Block', self, ' Input', i)
for i in outputs:
print ('Block', self, ' Output', i)
print (self, len(args), len(inputs), inputs)
if len(args) < len(inputs):
raise ValueError('Not enough arguments for input')
args = list(args)
for i, input in enumerate(inputs):
input.index = i
source = args.pop(0)
if isinstance(source, Block):
source = source.output
print ('Assign output', self, source)
input.source = source
for i, output in enumerate(outputs):
output.index = i
input_types = [x.type for x in inputs]
output_types = [x.type for x in outputs]
name = self.__class__.__name__
if hasattr(self, 'general_work'):
self.gr_block = gr.basic_block(name, input_types, output_types)
self.gr_block.general_work = self.general_work
self.init(*args, **kwargs)
assert hasattr(self, 'gr_block')
class InOutBlock(Block):
"A base class for the default case of a block with input and one output"
input = Input()
output = Output(input)
class BandPass(InOutBlock):
def init(self, lo, hi):
nyquist = self.input.sample_rate / 2.0
Wp = [lo / nyquist, hi / nyquist]
#Ws = [(lo - 1) / nyquist, (hi+1) / nyquist]
#b, a = scipy.signal.iirdesign(Wp, Ws, 0.1, 60.0)
b, a = scipy.signal.iirfilter(6, Wp, btype='bandpass',
ftype='ellip', rp=0.1, rs=60.0)
#self.gr_block = filter.iir_filter_ffd(a, b, oldstyle=False)
self.gr_block = filter.iir_filter_ffd(b, a, oldstyle=False)
class NotchFilter(InOutBlock):
def init(self, freq=50.0, mod=0.9):
theta = 2 * np.pi * 50 / self.input.sample_rate
zero = np.exp(np.array([1j, -1j]) * theta)
pole = mod * zero
a, b = np.poly(pole), np.poly(zero)
#notch_ab = numpy.poly(zero), numpy.poly(pole)
#notch_ab = scipy.signal.iirfilter(32, [30.0 / 125], btype='low')
self.gr_block = filter.iir_filter_ffd(b, a, oldstyle=False)
class RMS(InOutBlock):
def init(self, alpha=0.01):
self.gr_block = blocks.rms_ff(alpha)
class DCBlock(InOutBlock):
def init(self, taps=16):
self.gr_block = filter.dc_blocker_ff(16, long_form=False)
class ExponentialAverage(InOutBlock):
def init(self, lookback = 1.0):
samples = length * self.input.sample_rate
scale = 1.0 / samples
self.gr_block = blocks.moving_average_ff(int(samples), scale)
def general_work(self, input_items, output_items):
print ('BarSpectrogram work', len(input_items[0]), output_items, input_items[0][0])
self.gr_block.consume_each(1)
self.gr_block.produce_each(1)
output_items[0][0] = result
self.buffer = input_items[0][-len(self.win):]
return 0
class Oscilloscope(Block):
input = Input()
def init(self, history=512, autoscale=True):