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self.widget = pg.PlotWidget()
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self.widget.block = self
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self.gr_block.set_history(history)
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self.plot = self.widget.plot()
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self.plot.setPen(QtGui.QColor(self.input.color))
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#self.widget.setYRange(*self.yrange)
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self.widget.enableAutoRange('y', 0.95 if autoscale else False)
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self.buffer = []
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self.timer = QtCore.QTimer()
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self.timer.timeout.connect(self.updateGUI)
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self.timer.start(100)
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def general_work(self, input_items, output_items):
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#print ('Oscilloscope work', len(input_items[0]), output_items, input_items[0][0])
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# TODO: Make relative to update rate
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self.gr_block.consume_each(5)
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self.buffer = input_items[0]
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return 0
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def updateGUI(self):
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self.plot.setData(self.buffer)
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self.widget.update()
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class BarGraph(Block):
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inputs = InputDict()
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def init(self):
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pass
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class BarSpectrogram(Block):
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input = Input()
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def init(self, lo=0, hi=125, bins=256, yrange=750, ratio=False):
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self.widget = pg.PlotWidget()
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self.widget.setLabel('bottom', 'Frequency', units='Hz')
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self.bars = pg.BarGraphItem()
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self.win = np.hanning(bins)
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self.win = np.blackman(bins)
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#self.win = np.ones(bins)
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self.lo, self.hi = lo, hi
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self.ratio = ratio
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FS = self.input.sample_rate
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self.gr_block.set_history(bins)
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#num_bars = int(round((self.bins - 1) * (self.hi - self.lo) / FS))
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# This is total bullshit:
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num_bars = len(np.zeros(bins)[lo: hi])
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x = np.linspace(self.lo, self.hi, num_bars)
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self.bars = pg.BarGraphItem(x=x, height=range(num_bars), width=1.0)
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self.bars.setOpts(brushes=[pg.hsvColor(float(x) / num_bars) for x in range(num_bars)])
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self.widget.addItem(self.bars)
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# TODO: Better autoranging features
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#self.plot.enableAutoRange('xy', False)
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self.widget.setYRange(0, yrange)
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self.widget.enableAutoRange('y', 0.95)
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self.buffer = np.zeros(bins)
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self.timer = QtCore.QTimer()
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self.timer.timeout.connect(self.updateGUI)
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self.timer.start(10)
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def general_work(self, input_items, output_items):
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#print ('BarSpectrogram work', len(input_items[0]), output_items, input_items[0][0])
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self.gr_block.consume_each(16)
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self.buffer = input_items[0][-len(self.win):]
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return 0
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def updateGUI(self):
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C = np.fft.rfft(self.buffer * self.win)
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C = abs(C)
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lo, hi = self.lo, self.hi
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data = C[lo : hi]
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