A new project I'm working on requires real-time analysis of soundcard input data, and I made a minimal case example of how to do this in a cross-platform way using python 3, numpy, and PyQt. Previous posts compared performance of the matplotlib widget vs PyQtGraph plotwidget and I've been working with PyQtGraph ever since. For static figures matplotlib is wonderful, but for fast responsive applications I'm leaning toward PyQtGraph. The full source for this project is on a github page, but here's a summary of the project.
I made the UI with QT Designer. The graphs are QGraphicsView widgets promoted to a pyqtgraph PlotWidget. I describe how to do this in my previous post. Here's the content of the primary program:
from PyQt4 import QtGui,QtCore import sys import ui_main import numpy as np import pyqtgraph import SWHear class ExampleApp(QtGui.QMainWindow, ui_main.Ui_MainWindow): def __init__(self, parent=None): pyqtgraph.setConfigOption('background', 'w') #before loading widget super(ExampleApp, self).__init__(parent) self.setupUi(self) self.grFFT.plotItem.showGrid(True, True, 0.7) self.grPCM.plotItem.showGrid(True, True, 0.7) self.maxFFT=0 self.maxPCM=0 self.ear = SWHear.SWHear() self.ear.stream_start() def update(self): if not self.ear.data is None and not self.ear.fft is None: pcmMax=np.max(np.abs(self.ear.data)) if pcmMax>self.maxPCM: self.maxPCM=pcmMax self.grPCM.plotItem.setRange(yRange=[-pcmMax,pcmMax]) if np.max(self.ear.fft)>self.maxFFT: self.maxFFT=np.max(np.abs(self.ear.fft)) self.grFFT.plotItem.setRange(yRange=[0,self.maxFFT]) self.pbLevel.setValue(1000*pcmMax/self.maxPCM) pen=pyqtgraph.mkPen(color='b') self.grPCM.plot(self.ear.datax,self.ear.data, pen=pen,clear=True) pen=pyqtgraph.mkPen(color='r') self.grFFT.plot(self.ear.fftx[:500],self.ear.fft[:500], pen=pen,clear=True) QtCore.QTimer.singleShot(1, self.update) # QUICKLY repeat if __name__=="__main__": app = QtGui.QApplication(sys.argv) form = ExampleApp() form.show() form.update() #start with something app.exec_() print("DONE")
This project uses a gutted version of the SWHEar class which I still haven't released on githib yet. It will likely have its own project folder. For now, take this project with a grain of salt. The primary advantage of this class is that it makes it easy to use PyAudio to automatically detect input sound cards, channels, and sample rates which are likely to succeed without requiring the user to enter any information.
All files used for this project are in a GitHub folder
2016-09-05: Okko adapted this project into a screenlet (cross platform) which also includes an installer for Windows: https://github.com/ninlith/audio-visualizer-screenlet