Stimuli changes with every frame being displayed. - psychopy

I have a bit of code (displayed below) that is supposed to display the stimulus for 10 frames. We need pretty exact display times, so using number of frames is a must instead of core.wait(xx) as the display time won't be as precise.
Instead of drawing the stimuli, and leaving it for another 9 frames - the stimuli is re-drawn for every frame.
# Import what is needed
import numpy as np
from psychopy import visual, event, core, logging
from math import sin, cos
import random, math
win = visual.Window(size=(1366, 768), fullscr=True, screen=0, allowGUI=False, allowStencil=False,
monitor='testMonitor', color=[0,0,0], colorSpace='rgb',
blendMode='avg', useFBO=True,
units='deg')
### Definitions of libraries
'''Parameters :
numpy - python package of numerical computations
visual - where all visual stimulus live
event - code to deal with mouse + keyboard input
core - general function for timing & closing the program
logging - provides function for logging error and other messages to one file
random - options for creating arrays of random numbers
sin & cos - for geometry and trigonometry
math - mathematical operations '''
# this is supposed to record all frames
win.setRecordFrameIntervals(True)
win._refreshThreshold=1/65.0+0.004 #i've got 65Hz monitor and want to allow 4ms tolerance
#set the log module to report warnings to the std output window (default is errors only)
logging.console.setLevel(logging.WARNING)
nIntervals=5
# Create space variables and a window
lineSpaceX = 0.55
lineSpaceY = 0.55
patch_orientation = 45 # zero is vertical, going anti-clockwise
surround_orientation = 90
#Jitter values
g_posJitter = 0.05 #gaussian positional jitter
r_posJitter = 0.05 #random positional jitter
g_oriJitter = 5 #gaussian orientation jitter
r_oriJitter = 5 #random orientation jitter
#create a 1-Dimentional array
line = np.array(range(38)) #with values from (0-37) #possibly not needed 01/04/16 DK
#Region where the rectangular patch would appear
#x_rand=random.randint(1,22) #random.randint(Return random integers from low (inclusive) to high (exclusive).
#y_rand=random.randint(1,25)
x_rand=random.randint(6,13) #random.randint(Return random integers from low (inclusive) to high (inclusive).
y_rand=random.randint(6,16)
#rectangular patch dimensions
width=15
height=12
message = visual.TextStim(win,pos=(0.0,-12.0),text='...Press SPACE to continue...')
fixation = visual.TextStim(win, pos=(0.0,0.0), text='X')
# Initialize clock to record response time
rt_clock = core.Clock()
#Nested loop to draw anti-aliased lines on grid
#create a function for this
def myStim():
for x in xrange(1,33): #32x32 grid. When x is 33 will not execute loop - will stop
for y in xrange(1,33): #When y is 33 will not execute loop - will stop
##Define x & y value (Gaussian distribution-positional jitter)
x_pos = (x-32/2-1/2 )*lineSpaceX + random.gauss(0,g_posJitter) #random.gauss(mean,s.d); -1/2 is to center even-numbered stimuli; 32x32 grid
y_pos = (y-32/2-1/2 )*lineSpaceY + random.gauss(0,g_posJitter)
if (x >= x_rand and x < x_rand+width) and (y >= y_rand and y < y_rand+height): # note only "=" on one side
Line_Orientation = random.gauss(patch_orientation,g_oriJitter) #random.gauss(mean,s.d) - Gaussian func.
else:
Line_Orientation = random.gauss(surround_orientation,g_oriJitter) #random.gauss(mean,s.d) - Gaussian func.
#Line_Orientation = random.gauss(Line_Orientation,g_oriJitter) #random.gauss(mean,s.d) - Gaussian func.
#stimOri = random.uniform(xOri - r_oriJitter, xOri + r_oriJitter) #random.uniform(A,B) - Uniform func.
visual.Line(win, units = "deg", start=(0,0), end=(0.0,0.35), pos=(x_pos,y_pos), ori=Line_Orientation, autoLog=False).draw() #Gaussian func.
for frameN in range (10):
myStim()
win.flip()
print x_rand, y_rand
print keys, rt #display response and reaction time on screen output window
I have tried to use the following code to keep it displayed (by not clearing the buffer). But it just draws over it several times.
for frameN in range(10):
myStim()
win.flip(clearBuffer=False)
I realize that the problem could be because I have .draw() in the function that I have defined def myStim():. However, if I don't include the .draw() within the function - I won't be able to display the stimuli.
Thanks in advance for any help.

If I understand correctly, the problem you are facing is that you have to re-draw the stimulus on every flip, but your current drawing function also recreates the entire (random) stimulus, so:
the stimulus changes on each draw between flips, although you need it to stay constant, and
you get a (on some systems quite massive) performance penalty by re-creating the entire stimulus over and over again.
What you want instead is: create the stimulus once, in its entirety, before presentation; and then have this pre-generated stimulus drawn on every flip.
Since your stimulus consists of a fairly large number of visual elements, I would suggest using a class to store the stimulus in one place.
Essentially, you would replace your myStim() function with this class (note that I stripped out most comments, re-aligned the code a bit, and simplified the if statement):
class MyStim(object):
def __init__(self):
self.lines = []
for x in xrange(1, 33):
for y in xrange(1, 33):
x_pos = ((x - 32 / 2 - 1 / 2) * lineSpaceX +
random.gauss(0, g_posJitter))
y_pos = ((y - 32 / 2 - 1 / 2) * lineSpaceY +
random.gauss(0, g_posJitter))
if ((x_rand <= x < x_rand + width) and
(y_rand <= y < y_rand + height)):
Line_Orientation = random.gauss(patch_orientation,
g_oriJitter)
else:
Line_Orientation = random.gauss(surround_orientation,
g_oriJitter)
current_line = visual.Line(
win, units="deg", start=(0, 0), end=(0.0, 0.35),
pos=(x_pos, y_pos), ori=Line_Orientation,
autoLog=False
)
self.lines.append(current_line)
def draw(self):
[line.draw() for line in self.lines]
What this code does on instantiation is in principle identical to your myStim() function: it creates a set of (random) lines. But instead of drawing them onto the screen right away, they are all collected in the list self.lines, and will remain there until we actually need them.
The draw() method traverses through this list, element by element (that is, line by line), and calls every line's draw() method. Note that the stimuli do not have to be re-created every time we want to draw the whole set, but instead we just draw the already pre-created lines!
To get this working in practice, you first need to instantiate the MyStim class:
myStim = MyStim()
Then, whenever you want to present the stimulus, all you have to do is
myStim.draw()
win.flip()
Here is the entire, modified code that should get you started:
import numpy as np
from psychopy import visual, event, core, logging
from math import sin, cos
import random, math
win = visual.Window(size=(1366, 768), fullscr=True, screen=0, allowGUI=False, allowStencil=False,
monitor='testMonitor', color=[0,0,0], colorSpace='rgb',
blendMode='avg', useFBO=True,
units='deg')
# this is supposed to record all frames
win.setRecordFrameIntervals(True)
win._refreshThreshold=1/65.0+0.004 #i've got 65Hz monitor and want to allow 4ms tolerance
#set the log module to report warnings to the std output window (default is errors only)
logging.console.setLevel(logging.WARNING)
nIntervals=5
# Create space variables and a window
lineSpaceX = 0.55
lineSpaceY = 0.55
patch_orientation = 45 # zero is vertical, going anti-clockwise
surround_orientation = 90
#Jitter values
g_posJitter = 0.05 #gaussian positional jitter
r_posJitter = 0.05 #random positional jitter
g_oriJitter = 5 #gaussian orientation jitter
r_oriJitter = 5 #random orientation jitter
x_rand=random.randint(6,13) #random.randint(Return random integers from low (inclusive) to high (inclusive).
y_rand=random.randint(6,16)
#rectangular patch dimensions
width=15
height=12
message = visual.TextStim(win,pos=(0.0,-12.0),text='...Press SPACE to continue...')
fixation = visual.TextStim(win, pos=(0.0,0.0), text='X')
# Initialize clock to record response time
rt_clock = core.Clock()
class MyStim(object):
def __init__(self):
self.lines = []
for x in xrange(1, 33):
for y in xrange(1, 33):
x_pos = ((x - 32 / 2 - 1 / 2) * lineSpaceX +
random.gauss(0, g_posJitter))
y_pos = ((y - 32 / 2 - 1 / 2) * lineSpaceY +
random.gauss(0, g_posJitter))
if ((x_rand <= x < x_rand + width) and
(y_rand <= y < y_rand + height)):
Line_Orientation = random.gauss(patch_orientation,
g_oriJitter)
else:
Line_Orientation = random.gauss(surround_orientation,
g_oriJitter)
current_line = visual.Line(
win, units="deg", start=(0, 0), end=(0.0, 0.35),
pos=(x_pos, y_pos), ori=Line_Orientation,
autoLog=False
)
self.lines.append(current_line)
def draw(self):
[line.draw() for line in self.lines]
myStim = MyStim()
for frameN in range(10):
myStim.draw()
win.flip()
# Clear the screen
win.flip()
print x_rand, y_rand
core.quit()
Please do note that even with this approach, I am dropping frames on a 3-year-old laptop computer with relatively weak integrated graphics chip. But I suspect a modern, fast GPU would be able to handle this amount of visual objects just fine. In the worst case, you could pre-create a large set of stimuli, save them as a bitmap file via win.saveMovieFrames(), and present them as a pre-loaded SimpleImageStim during your actual study.

Related

pyqtgraph : how to allow a plot to only increase its range

I have a xy-plot that updates with new data.
At the first iteration, the XAxis range is automatically set to display the data. On the next iteration, new data is generated, with a potentially smaller min value, and a potentialy bigger max value. By default, pyqtgraph will adjust the range to again display the new data.
What I want is to keep the 'biggest' ranges, that is keep the smallest min limit between both data, and the biggest max limit.
Here a simple example inspired by the 'AutoRange' example from the doc :
# from the auto-range example : from pyqtgraph import examples; examples.run()
import time
import numpy as np
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore
app = pg.mkQApp("Plot Auto Range Example")
win = pg.GraphicsLayoutWidget(show=True, title="Plot auto-range examples")
win.resize(800,600)
win.setWindowTitle('pyqtgraph example: PlotAutoRange')
xs = np.random.normal(size=100)
ys = np.random.normal(size=100)
p2 = win.addPlot(title="Auto Pan Only")
# AutoPan is not what I want : p2.setAutoPan(y=True)
# maybe something else here like
# p2.setRangeRule(x='onlyExtend')
# --> so that the x limits will only grow based on
# new data
curve = p2.plot(xs, ys)
t0 = time.time()
def update():
xs = np.random.normal(size=100)
ys = np.random.normal(size=100)
# other possibility : manually check the old and new limits, and keep
# the min/max I want, and then set it to the axes....
old_box = p2.viewRect()
old_xmin = old_box.left()
old_xmax = old_box.right()
# update the curve to new data (this is always wanted)
curve.setData(xs, ys)
new_box = p2.viewRect()
new_xmin = new_box.left()
new_xmax = new_box.right()
# .... this causes the xlimits to grow indefinetely
# p2.setRange(xRange=(min(old_xmin, new_xmin), max(old_xmax, new_xmax)))
timer = QtCore.QTimer()
timer.timeout.connect(update)
timer.start(50)
if __name__ == '__main__':
pg.exec()
As you can see I tried manually get/check/set the limits but ended up with endlessly increasing xlimits (way bigger than needed just to display data).
I guess it comes down to a difference between the limits, the range, the viewbox, the rectangles, and so on. To be fair I am not familiar with the QGraphicView framework.

How to re-evaluate Gekko objective while minimizing objective's parameters

Apologies in advance, I just started to learn Gekko to see if I can use it for a project. I'm trying to optimize the win rate while playing a game with very finite game-states (50 ^ 2) and options per turn (0-10 inclusive).
From what I understand, I can use the m.solve() Gekko function to minimize the win rate of the opponent which I've set up here:
PLAYER_MAX_SCORE = 50 #Score player needs to win
OPPONENT_MAX_SCORE = 50 #Score opponent needs to win
#The opponent's current strategy: always roll 4 dice per turn
OPPONENT_MOVE = 4
m = GEKKO()
m.options.SOLVER = 1
"""
player_moves is a 2-d array where:
- the row represents player's current score
- the column represents opponent's current score
- the element represents the optimal move for the above game state
Thus the player's move for a game is player_moves[pScore, oScore].value.value
"""
player_moves = m.Array(m.Var, (PLAYER_MAX_SCORE, OPPONENT_MAX_SCORE), value=3, lb=0, ub=10, integer=True)
m.Obj(objective(player_moves, OPPONENT_MOVE, PLAYER_MAX_SCORE, OPPONENT_MAX_SCORE, 100))
m.solve(disp=False)
For reference, objective is a function that returns the win rate of the opponent based on how the current player acts (represented in player_moves).
The only issue is that m.solve() only calls the objective function once and then immediately returns the "solved" values in the player_moves array (which turn out to just be the initial values when player_moves was defined). I want m.solve() to call the objective function multiple times to determine if the new opponent's win rate is decreasing or increasing.
Is this possible with Gekko? Or is there a different library I should use for this type of problem?
Gekko creates a symbolic representation of the optimization problem that is compiled into byte-code. For this reason, the objective function must be expressed with Gekko variables and equations. For black-box models that do not use Gekko variables, an alternative is to use scipy.optimize.minimize(). There is a comparison of Gekko and Scipy.
Scipy
import numpy as np
from scipy.optimize import minimize
def objective(x):
return x[0]*x[3]*(x[0]+x[1]+x[2])+x[2]
def constraint1(x):
return x[0]*x[1]*x[2]*x[3]-25.0
def constraint2(x):
sum_eq = 40.0
for i in range(4):
sum_eq = sum_eq - x[i]**2
return sum_eq
# initial guesses
n = 4
x0 = np.zeros(n)
x0[0] = 1.0
x0[1] = 5.0
x0[2] = 5.0
x0[3] = 1.0
# show initial objective
print('Initial Objective: ' + str(objective(x0)))
# optimize
b = (1.0,5.0)
bnds = (b, b, b, b)
con1 = {'type': 'ineq', 'fun': constraint1}
con2 = {'type': 'eq', 'fun': constraint2}
cons = ([con1,con2])
solution = minimize(objective,x0,method='SLSQP',\
bounds=bnds,constraints=cons)
x = solution.x
# show final objective
print('Final Objective: ' + str(objective(x)))
# print solution
print('Solution')
print('x1 = ' + str(x[0]))
print('x2 = ' + str(x[1]))
print('x3 = ' + str(x[2]))
print('x4 = ' + str(x[3]))
Gekko
from gekko import GEKKO
import numpy as np
#Initialize Model
m = GEKKO()
#initialize variables
x1,x2,x3,x4 = [m.Var(lb=1,ub=5) for i in range(4)]
#initial values
x1.value = 1
x2.value = 5
x3.value = 5
x4.value = 1
#Equations
m.Equation(x1*x2*x3*x4>=25)
m.Equation(x1**2+x2**2+x3**2+x4**2==40)
#Objective
m.Minimize(x1*x4*(x1+x2+x3)+x3)
#Solve simulation
m.solve()
#Results
print('')
print('Results')
print('x1: ' + str(x1.value))
print('x2: ' + str(x2.value))
print('x3: ' + str(x3.value))
print('x4: ' + str(x4.value))

Matplotlib Stopping an Animation

The following code is the function that's used to create an animation of appearing and then fading-away points on a Matplotlib basemap. I was wondering how it's possible to slow the interval down between each point? In this case, I have set frames = 62, because there are 62 points. However, changing the interval to a larger value doesn't seem to slow down the interval between points. Am I missing something here? The attached animation function and GIF is attached below. The rest of the code isn't here, because I didn't think it was relevant to the question. Thanks.
def animate(frame):
eq_num = frame % len(X)
i = frame % len(P)
P['colour'][:,3] = np.maximum(0, P['colour'][:,3] - 1.0/len(P))
P['size'] += P['growth']
magnitude = X['magnitude'][eq_num]
P['epicentre'][i] = m(*X['epicentre'][eq_num])
P['size'][i] = 5
P['growth'][i]= np.exp(magnitude) * 0.1
if magnitude < 4:
P['colour'][i] = 0,0,1,1
else:
P['colour'][i] = 1,0,0,1
scatter.set_edgecolors(P['colour'])
scatter.set_facecolors(P['colour']*(1,1,1,0.25))
scatter.set_sizes(P['size'])
scatter.set_offsets(P['epicentre'])
return scatter,
ani = FuncAnimation(fig,animate,frames=62,interval=1000,blit=False)
ani.save('animation.gif', writer='imagemagick', fps=100)
#plt.show()

Plot random points a specified distance apart

I'm trying to come up with a function that plots n points inside the unit circle, but I need them to be sufficiently spread out.
ie. something that looks like this:
Is it possible to write a function with two parameters, n (number of points) and min_d (minimum distance apart) such that the points are:
a) equidistant
b) no pairwise distance exceeds a given min_d
The problem with sampling from a uniform distribution is that it could happen that two points are almost on top of each other, which I do not want to happen. I need this kind of input for a network diagram representing node clusters.
EDIT: I have found an answer to a) here: Generator of evenly spaced points in a circle in python, but b) still eludes me.
At the time this answer was provided, the question asked for random numbers. This answer thus gives a solution drawing random numbers. It ignores any edits made to the question afterwards.
On may simply draw random points and for each one check if the condition of the minimum distance is fulfilled. If not, the point can be discarded. This can be done until a list is filled with enough points or some break condition is met.
import numpy as np
import matplotlib.pyplot as plt
class Points():
def __init__(self,n=10, r=1, center=(0,0), mindist=0.2, maxtrials=1000 ) :
self.success = False
self.n = n
self.r = r
self.center=np.array(center)
self.d = mindist
self.points = np.ones((self.n,2))*10*r+self.center
self.c = 0
self.trials = 0
self.maxtrials = maxtrials
self.tx = "rad: {}, center: {}, min. dist: {} ".format(self.r, center, self.d)
self.fill()
def dist(self, p, x):
if len(p.shape) >1:
return np.sqrt(np.sum((p-x)**2, axis=1))
else:
return np.sqrt(np.sum((p-x)**2))
def newpoint(self):
x = (np.random.rand(2)-0.5)*2
x = x*self.r-self.center
if self.dist(self.center, x) < self.r:
self.trials += 1
if np.all(self.dist(self.points, x) > self.d):
self.points[self.c,:] = x
self.c += 1
def fill(self):
while self.trials < self.maxtrials and self.c < self.n:
self.newpoint()
self.points = self.points[self.dist(self.points,self.center) < self.r,:]
if len(self.points) == self.n:
self.success = True
self.tx +="\n{} of {} found ({} trials)".format(len(self.points),self.n,self.trials)
def __repr__(self):
return self.tx
center =(0,0)
radius = 1
p = Points(n=40,r=radius, center=center)
fig, ax = plt.subplots()
x,y = p.points[:,0], p.points[:,1]
plt.scatter(x,y)
ax.add_patch(plt.Circle(center, radius, fill=False))
ax.set_title(p)
ax.relim()
ax.autoscale_view()
ax.set_aspect("equal")
plt.show()
If the number of points should be fixed, you may try to run find this number of points for decreasing distances until the desired number of points are found.
In the following case, we are looking for 60 points and start with a minimum distance of 0.6 which we decrease stepwise by 0.05 until there is a solution found. Note that this will not necessarily be the optimum solution, as there is only maxtrials of retries in each step. Increasing maxtrials will of course bring us closer to the optimum but requires more runtime.
center =(0,0)
radius = 1
mindist = 0.6
step = 0.05
success = False
while not success:
mindist -= step
p = Points(n=60,r=radius, center=center, mindist=mindist)
print p
if p.success:
break
fig, ax = plt.subplots()
x,y = p.points[:,0], p.points[:,1]
plt.scatter(x,y)
ax.add_patch(plt.Circle(center, radius, fill=False))
ax.set_title(p)
ax.relim()
ax.autoscale_view()
ax.set_aspect("equal")
plt.show()
Here the solution is found for a minimum distance of 0.15.

Verlet integrator + friction

I have been following "A Verlet based approach for 2D game physics" on Gamedev.net and I have written something similar.
The problem I am having is that the boxes slide along the ground too much.
How can I add a simple rested state thing where the boxes will have more friction and only slide a tiny bit?
Just introduce a small, constant acceleration on moving objects that points in the direction opposite to the motion. And make sure it can't actually reverse the motion; if you detect that in an integration step, just set the velocity to zero.
If you want to be more realistic, the acceleration should derive from a force which is proportional to the normal force between the object and the surface it's sliding on.
You can find this in any basic physics text, as "kinetic friction" or "sliding friction".
At the verlet integration: r(t)=2.00*r(t-dt)-1.00*r(t-2dt)+2at²
change the multipliers to 1.99 and 0.99 for friction
Edit: this is more true:
r(t)=(2.00-friction_mult.)*r(t-dt)-(1.00-friction_mult.)*r(t-2dt)+at²
Here is a simple time stepping scheme (symplectic Euler method with manually resolved LCP) for a box with Coulomb friction and a spring (frictional oscillator)
mq'' + kq + mu*sgn(q') = F(t)
import numpy as np
import matplotlib.pyplot as plt
q0 = 0 # initial position
p0 = 0 # initial momentum
t_start = 0 # initial time
t_end = 10 # end time
N = 500 # time points
m = 1 # mass
k = 1 # spring stiffness
muN = 0.5 # friction force (slip and maximal stick)
omega = 1.5 # forcing radian frequency [RAD]
Fstat = 0.1 # static component of external force
Fdyn = 0.6 # amplitude of harmonic external force
F = lambda tt,qq,pp: Fstat + Fdyn*np.sin(omega*tt) - k*qq - muN*np.sign(pp) # total force, note sign(0)=0 used to disable friction
zero_to_disable_friction = 0
omega0 = np.sqrt(k/m)
print("eigenfrequency f = {} Hz; eigen period T = {} s".format(omega0/(2*np.pi), 2*np.pi/omega0))
print("forcing frequency f = {} Hz; forcing period T = {} s".format(omega/(2*np.pi), 2*np.pi/omega))
time = np.linspace(t_start, t_end, N) # time grid
h = time[1] - time[0] # time step
q = np.zeros(N+1) # position
p = np.zeros(N+1) # momentum
absFfriction = np.zeros(N+1)
q[0] = q0
p[0] = p0
for n, tn in enumerate(time):
p1slide = p[n] + h*F(tn, q[n], p[n]) # end-time momentum, assuming sliding
q1slide = q[n] + h*p1slide/m # end-time position, assuming sliding
if p[n]*p1slide > 0: # sliding goes on
q[n+1] = q1slide
p[n+1] = p1slide
absFfriction[n] = muN
else:
q1stick = q[n] # assume p1 = 0 at t=tn+h
Fstick = -p[n]/h - F(tn, q1stick, zero_to_disable_friction) # friction force needed to stop at t=tn+h
if np.abs(Fstick) <= muN:
p[n+1] = 0 # sticking
q[n+1] = q1stick
absFfriction[n] = np.abs(Fstick)
else: # sliding starts or passes zero crossing of velocity
q[n+1] = q1slide # possible refinements (adapt to slip-start or zero crossing)
p[n+1] = p1slide
absFfriction[n] = muN