Animated plots of log files

Here's a animated data logger with a sliding range, autoscaling on y, and some other bells and widgets. It reads from a selected file, and plots the values as they are updated. The slider allows a selectable history range. All thanks to the awesomeness that is Matplotlib!

	# Plot log files
	# version 3
	# Amar
	# Changes: v2: 2012/11/27: Date & time display using native matplotlib, instead of unix timestamp
	# v3: 2013/10/10: Rewritten completely using matplotlib animation, and slider for selectable range
	import numpy as np
	import random
	import os
	os.chdir("c:/data/googledrive/logs")
	from datetime import datetime
	import matplotlib
	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
	import sys
	if sys.version_info[0] < 3:
	import Tkinter as Tk
	else:
	import tkinter as Tk
	import tkFileDialog
	if __name__ == '__main__':
	root = Tk.Tk()
	try:
	filename = tkFileDialog.askopenfilename(parent=root,title='Choose file to plot')
	root.wm_title(filename)
	fig = Figure()
	ax = fig.add_subplot(111)
	ax.autoscale(enable=True)
	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)
	# For showing dates
	fig.autofmt_xdate()
	ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
	# Set titles and comments
	f = file(filename,'r')
	titleLine = f.readline()[2:] # first line is the log comment
	commentLine = f.readline() # second line contains the column labels
	commentLine = commentLine[1:-1]
	comments = commentLine.split("\t")
	f.close()
	compactFileName = filename.split('/')[-1]
	ax.set_title(titleLine)
	ax.set_ylabel(comments[1])
	axcolor = 'lightgoldenrodyellow'
	axNumber = fig.add_axes([0.25, 0.2, 0.65, 0.03],axisbg=axcolor)
	sNumber = Slider(axNumber, '# of pts', 1000, 10000, valfmt='%0.0f', valinit=5000)
	sNumber.valtext.set_visible(False)
	#sNumber.on_changed(duh)
	x0,y0 = np.loadtxt(filename,unpack=True)
	x0,y0 = x0[-sNumber.val::5],y0[-sNumber.val::5]
	x0 = map(datetime.fromtimestamp,x0)
	line, = ax.plot(x0,y0,linewidth=1.2,color = cm.spectral(random.randrange(0,255)))
	def update(dummyVar):
	try:
	x,y = np.loadtxt(filename,unpack=True) #np.random.rand(10),np.random.rand(10)
	x1,y1 = x[-sNumber.val::5],y[-sNumber.val::5]
	yrange = max(y1) - min(y1)
	yavg = (max(y1) + min(y1))/2.
	ylims = yavg - 1.05*yrange/2., yavg + 1.05*yrange/2.
	ax.set_ylim(ylims[0],ylims[1])
	x1 = map(datetime.fromtimestamp,x1)
	ax.set_xlim(min(x1),max(x1))
	line.set_xdata(x1)
	line.set_ydata(y1)
	except ValueError or UnboundLocalError: pass
	return line,
	def data_gen():
	while True: yield True
	ani = animation.FuncAnimation(fig, update, data_gen, interval=3000)
	except IOError: pass
	Tk.mainloop()

view raw plotLogfile.py hosted with ❤ by GitHub