Spectrum analyzer with a Rigol scope

Here's some code to turn a Rigol oscilloscope into a spectrum analyzer.

You can grab the header file here: rigolScope.py

	# Get waveforms from Rigol scope and do spectrum analysis, fast version
	# Amar Vutha
	# version 1, 2013-12-30
	from __future__ import division
	import pylab as plt
	import numpy as np
	from scipy import signal
	from scipy.constants import e
	import os
	from datetime import date
	import time
	from functools import partial
	import matplotlib
	import matplotlib.pyplot as plt
	import matplotlib.animation as animation
	from matplotlib.widgets import Slider, Button, RadioButtons
	import matplotlib.dates as mdates
	import matplotlib.cm as cm
	matplotlib.use('TkAgg')
	from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
	from matplotlib.figure import Figure
	os.chdir("c:/data/googledrive/code")
	import rigolScope
	import sys
	if sys.version_info[0] < 3:
	import Tkinter as Tk
	else:
	import tkinter as Tk
	import tkFileDialog
	from collections import deque
	Navg=64
	averagingQueue = deque([])
	def powerSpectrum(y,samplingRate):
	#win = signal.get_window( ('gaussian',600/10),600 )
	Fy = np.fft.fft(y)/np.sqrt(len(y) * samplingRate)
	f = np.fft.fftfreq(len(y),1/samplingRate)
	return f[1:],(2 * np.abs(Fy)**2)[1:] # Throw away zero frequency component
	def powerSpectrum2(y,samplingRate):
	win = signal.get_window( ('gaussian',len(y)/7),len(y) )
	return signal.welch(y,fs=samplingRate,window=win,return_onesided=True, scaling='density')
	if __name__ == '__main__':
	root = Tk.Tk()
	root.wm_title("Spectrum Analyzer")
	os.chdir("c:/data/googledrive/logs")
	address = "USB0::0x1AB1::0x0588::DS1EB142003911::INSTR"
	#Alpha = "USB0::0x1AB1::0x0588::DS1EB142004009::INSTR"
	#Beta = "USB0::0x1AB1::0x0588::DS1EB142003911::INSTR"
	s = rigolScope.Scope(address)
	print s.id
	fig = Figure()
	ax = fig.add_subplot(111)
	ax2=ax.twinx()
	canvas = FigureCanvasTkAgg(fig, master=root)
	canvas.show()
	canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
	toolbar = NavigationToolbar2TkAgg( canvas, root )
	toolbar.update()
	canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
	t,V = s.fastGet()
	samplingRate = 1/(t[1] - t[0])
	f,W = powerSpectrum(V,samplingRate)
	df = f[1]-f[0]
	line, = ax.loglog(f,np.sqrt(W)/np.abs(np.average(V)),'b',lw=2)
	line2, = ax2.loglog(f,np.sqrt(W),'g',lw=2)
	Vtext_template = ' avg = %.2g V \n rms = %.2g V'
	text = ax.text(0.07,0.07,Vtext_template%(np.average(V), np.sqrt( np.trapz(W,dx=df) )),transform=ax.transAxes, \
	family='monospace',fontsize=20,weight='heavy')
	ax.set_xlim(min(f),max(f))
	ax.set_ylim(0.1 * min(np.sqrt(W))/np.abs(np.average(V)),10 * max(np.sqrt(W))/np.abs(np.average(V)))
	ax2.set_ylim(0.1* min(np.sqrt(W))/np.abs(np.average(V)),10 * max(np.sqrt(W))/np.abs(np.average(V)))
	ax.set_xlabel("Frequency, $f$ [Hz]")
	#ax.set_ylabel("SSB Spectral density, $\sqrt{W_V}$ [V/$\sqrt{\mathrm{Hz}}$]")
	ax.set_ylabel("NSR, $G_V$ [/$\sqrt{\mathrm{Hz}}$]",color='b')
	ax2.set_ylabel("Noise, $\sqrt{W_V}$ [V/$\sqrt{\mathrm{Hz}}$]",color='g')
	ax.grid(which='major',axis='both',linewidth=2)
	averagingQueue.append(W)
	def animate(i):
	t,V = s.fastGet()
	samplingRate = 1/(t[1] - t[0])
	f,W = powerSpectrum(V,samplingRate)
	averagingQueue.append(W)
	if len(averagingQueue) >= Navg: averagingQueue.popleft()
	Wavg = np.average(averagingQueue,axis=0)
	line.set_ydata(np.sqrt(Wavg)/np.abs(np.average(V))) # update the data
	line2.set_ydata(np.sqrt(Wavg)) # update the data
	text.set_text(Vtext_template%(np.average(V), np.sqrt( np.trapz(Wavg,dx=df) ) ))
	return (line,) + (line2,) + (text,)
	#Init only required for blitting to give a clean slate.
	def init():
	line.set_ydata(np.zeros(len(f)))
	line2.set_ydata(np.zeros(len(f)))
	text.set_text(" \n ")
	return (line,) + (line2,) + (text,)
	ani = animation.FuncAnimation(fig, animate, init_func=init,
	frames=25,interval=10, blit=True)
	Tk.mainloop()
	#plt.show()

view raw rigolScope-spectrumAnalyzer.py hosted with ❤ by GitHub