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