Timing check for OpenSesame Legacy Backend

legacy_timing_check.opensesame

# Generated by OpenSesame 0.25 (Dashy Darwin)
# Wed Feb  8 10:55:36 2012 (posix)
# 
# Copyright Sebastiaan Mathot (2010-2011)
# <http://www.cogsci.nl>
# 
set foreground "white"
set subject_parity "even"
set description "Default description"
set title "Legacy timing check"
set compensation "0"
set coordinates "relative"
set height "768"
set mouse_backend "legacy"
set width "1024"
set sampler_backend "legacy"
set keyboard_backend "legacy"
set background "black"
set subject_nr "0"
set canvas_backend "legacy"
set start "experiment"
set synth_backend "legacy"

define keyboard_response keyboard_response
	set description "Collects keyboard responses"
	set timeout "infinite"
	set flush "yes"

define sequence experiment
	set flush_keyboard "yes"
	set description "Runs a number of items in sequence"
	run timing_check "always"
	run keyboard_response "always"

define inline_script timing_check
	___run__
	from openexp.canvas import canvas
	import numpy as np
	
	speed = 2 # Rotation speed
	soa = 50 # SOA
	
	my_canvas1 = canvas(self.experiment)
	my_canvas1.circle(my_canvas1.xcenter()+100, my_canvas1.ycenter(), 50)
	
	my_canvas2 = canvas(self.experiment)
	my_canvas2.circle(my_canvas2.xcenter()-100, my_canvas2.ycenter(), 50)
	
	# Rotate the canvas and record timestamps
	l = []
	for i in range(0, 100):
		l.append(my_canvas1.show())
		self.sleep(soa)
		l.append(my_canvas2.show())
		self.sleep(soa)
		
	# Determine the actual SOA
	_l = []
	for i in range(len(l)-1):
		_l.append(l[i+1]-l[i])
		
	# Calculate the mean SOA and the standard deviation
	a = np.array(_l)
	m = a.mean()
	std = a.std()
	
	# Report the results
	my_canvas1.clear()
	my_canvas1.text('M=%.2f, STD=%.2f' % (m, std))
	my_canvas1.show()
	__end__
	set _prepare ""
	set description "Executes Python code"