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This plot displays the audio spectrum from the microphone.
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Based on updating_plot.py
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# Major library imports
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from numpy import zeros, linspace, short, fromstring, hstack, transpose, array
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# Enthought library imports
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from enthought.chaco.default_colormaps import jet
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from enthought.enable.api import Window, Component, ComponentEditor
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from enthought.traits.api import HasTraits, Instance, List, Range
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from enthought.traits.ui.api import Item, Group, View, Handler
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from enthought.enable.example_support import DemoFrame, demo_main
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from enthought.pyface.timer.api import Timer
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from enthought.chaco.api import (Plot, ArrayPlotData, HPlotContainer, VPlotContainer,
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AbstractMapper, LinePlot, LinearMapper, DataRange1D, OverlayPlotContainer)
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SPECTROGRAM_LENGTH = 50
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class WaterfallRenderer(LinePlot):
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# numpy arrays of the same length
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values = List(args=[])
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# Maps each array in values into a contrained, short screen space
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y2_mapper = Instance(AbstractMapper)
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_cached_data_pts = List()
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_cached_screen_pts = List()
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def _gather_points(self):
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if not self._cache_valid:
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if not self.index or len(self.values) == 0:
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index = self.index.get_data()
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if numindex == 0 or all(len(v)==0 for v in values) or all(numindex != len(v) for v in values):
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self._cached_data_pts = []
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self._cache_valid = True
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self._cached_data_pts = [transpose(array((index, v))) for v in values]
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self._cache_value = True
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def get_screen_points(self):
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return [self.map_screen(pts, i) for i, pts in enumerate(self._cached_data_pts)]
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def map_screen(self, data_array, data_offset=None):
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""" data_offset, if provided, is a float that will be mapped
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into screen space using self.value_mapper and then added to
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mapping data_array with y2_mapper. If data_offset is not
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provided, then y2_mapper is used.
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if len(data_array) == 0:
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x_ary, y_ary = transpose(data_array)
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sx = self.index_mapper.map_screen(x_ary)
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if data_offset is not None:
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dy = self.value_mapper.map_screen(data_offset)
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sy = self.y2_mapper.map_screen(y_ary) + dy
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sy = self.value_mapper.map_screen(y_ary)
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if self.orientation == "h":
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return transpose(array((sx, sy)))
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return transpose(array((sy, sx)))
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#============================================================================
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# Create the Chaco plot.
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#============================================================================
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def _create_plot_component(obj):
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# Setup the spectrum plot
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frequencies = linspace(0.0, float(SAMPLING_RATE)/2, num=NUM_SAMPLES/2)
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obj.spectrum_data = ArrayPlotData(frequency=frequencies)
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empty_amplitude = zeros(NUM_SAMPLES/2)
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obj.spectrum_data.set_data('amplitude', empty_amplitude)
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obj.spectrum_plot = Plot(obj.spectrum_data)
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spec_renderer = obj.spectrum_plot.plot(("frequency", "amplitude"), name="Spectrum",
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obj.spectrum_plot.padding = 50
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obj.spectrum_plot.title = "Spectrum"
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spec_range = obj.spectrum_plot.plots.values()[0][0].value_mapper.range
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obj.spectrum_plot.index_axis.title = 'Frequency (hz)'
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obj.spectrum_plot.value_axis.title = 'Amplitude'
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times = linspace(0.0, float(NUM_SAMPLES)/SAMPLING_RATE, num=NUM_SAMPLES)
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obj.time_data = ArrayPlotData(time=times)
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empty_amplitude = zeros(NUM_SAMPLES)
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obj.time_data.set_data('amplitude', empty_amplitude)
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obj.time_plot = Plot(obj.time_data)
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obj.time_plot.plot(("time", "amplitude"), name="Time", color="blue")
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obj.time_plot.padding = 50
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obj.time_plot.title = "Time"
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obj.time_plot.index_axis.title = 'Time (seconds)'
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obj.time_plot.value_axis.title = 'Amplitude'
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time_range = obj.time_plot.plots.values()[0][0].value_mapper.range
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time_range.low = -0.2
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time_range.high = 0.2
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values = [zeros(NUM_SAMPLES/2) for i in xrange(SPECTROGRAM_LENGTH)]
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p = WaterfallRenderer(index = spec_renderer.index, values = values,
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index_mapper = LinearMapper(range = obj.spectrum_plot.index_mapper.range),
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value_mapper = LinearMapper(range = DataRange1D(low=0, high=SPECTROGRAM_LENGTH)),
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y2_mapper = LinearMapper(low_pos=0, high_pos=8,
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range=DataRange1D(low=0, high=15)),
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obj.spectrogram_plot = p
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dummy.index_axis.mapper.range = p.index_mapper.range
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dummy.index_axis.title = "Frequency (hz)"
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container = HPlotContainer()
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container.add(obj.spectrum_plot)
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container.add(obj.time_plot)
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c2 = VPlotContainer()
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def get_audio_data():
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pa = pyaudio.PyAudio()
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stream = pa.open(format=pyaudio.paInt16, channels=1, rate=SAMPLING_RATE,
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input=True, frames_per_buffer=NUM_SAMPLES)
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audio_data = fromstring(stream.read(NUM_SAMPLES), dtype=short)
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normalized_data = audio_data / 32768.0
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return (abs(fft(normalized_data))[:NUM_SAMPLES/2], normalized_data)
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# HasTraits class that supplies the callable for the timer event.
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class TimerController(HasTraits):
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def onTimer(self, *args):
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spectrum, time = get_audio_data()
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self.spectrum_data.set_data('amplitude', spectrum)
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self.time_data.set_data('amplitude', time)
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spec_data = self.spectrogram_plot.values[1:] + [spectrum]
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self.spectrogram_plot.values = spec_data
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self.spectrum_plot.request_redraw()
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#============================================================================
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# Attributes to use for the plot view.
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title = "Audio Spectrum Waterfall"
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#============================================================================
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# Demo class that is used by the demo.py application.
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#============================================================================
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class DemoHandler(Handler):
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def closed(self, info, is_ok):
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""" Handles a dialog-based user interface being closed by the user.
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Overridden here to stop the timer once the window is destroyed.
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info.object.timer.Stop()
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class Demo(HasTraits):
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plot = Instance(Component)
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controller = Instance(TimerController, ())
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timer = Instance(Timer)
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Item('plot', editor=ComponentEditor(size=size),
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orientation = "vertical"),
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resizable=True, title=title,
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width=size[0], height=size[1]+25,
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def __init__(self, **traits):
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super(Demo, self).__init__(**traits)
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self.plot = _create_plot_component(self.controller)
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def edit_traits(self, *args, **kws):
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# Start up the timer! We should do this only when the demo actually
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# starts and not when the demo object is created.
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self.timer = Timer(20, self.controller.onTimer)
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return super(Demo, self).edit_traits(*args, **kws)
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def configure_traits(self, *args, **kws):
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# Start up the timer! We should do this only when the demo actually
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# starts and not when the demo object is created.
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self.timer = Timer(20, self.controller.onTimer)
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return super(Demo, self).configure_traits(*args, **kws)
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#============================================================================
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# Stand-alone frame to display the plot.
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#============================================================================
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from enthought.etsconfig.api import ETSConfig
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if ETSConfig.toolkit == "wx":
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class PlotFrame(DemoFrame):
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def _create_window(self):
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self.controller = TimerController()
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container = _create_plot_component(self.controller)
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# Bind the exit event to the onClose function which will force the
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# example to close. The PyAudio package causes problems that normally
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# prevent the user from closing the example using the 'X' button.
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# NOTE: I believe it is sufficient to just stop the timer-Vibha.
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self.Bind(wx.EVT_CLOSE, self.onClose)
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# Set the timer to generate events to us
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self.timer = wx.Timer(self, timerId)
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self.Bind(wx.EVT_TIMER, self.controller.onTimer, id=timerId)
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self.timer.Start(20.0, wx.TIMER_CONTINUOUS)
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# Return a window containing our plots
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return Window(self, -1, component=container)
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def onClose(self, event):
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elif ETSConfig.toolkit == "qt4":
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from enthought.qt import QtGui, QtCore
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class PlotFrame(DemoFrame):
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def _create_window(self):
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self.controller = TimerController()
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container = _create_plot_component(self.controller)
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# start a continuous timer
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self.timer = QtCore.QTimer()
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self.timer.timeout.connect(self.controller.onTimer)
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return Window(self, -1, component=container)
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def closeEvent(self, event):
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if getattr(self, "timer", None):
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return super(PlotFrame, self).closeEvent(event)
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if __name__ == "__main__":
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demo_main(PlotFrame, size=size, title=title)
added
removed
examples/advanced/spec_waterfall.py
