def draw(self):
pyp.pushMatrix()
pyp.noStroke()
pyp.fill(self.fill_color)
pyp.translate(*self.position)
self.draw_poly()
pyp.popMatrix()
def branches(height): height *= 0.66 # draw left and right branch for angle in [-0.5, 0.5]: p.pushMatrix() p.rotate(angle) p.line(0, 0, 0, -height) p.translate(0, -height) p.popMatrix()
def wrapped_f(): p.pushMatrix() p.translate(240, 0) p.applyMatrix( -1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1) f() p.popMatrix()
def drag_segment(i, head_x, head_y): # find the inclination of a segment with respect to X axis angle = math.atan2(head_y - y[i], head_x - x[i]) # find tail position by rotating a segment around its head point x[i] = head_x - math.cos(angle) * LENGTH y[i] = head_y - math.sin(angle) * LENGTH # draw a segment (tail to head) p.pushMatrix() p.translate(x[i], y[i]) p.rotate(angle) p.line(0, 0, LENGTH, 0) p.popMatrix()
def branches(height, angle): height *= 0.66 # finish branching when height is small if height < 3: return # draw left and right branch for angle in [-angle, angle]: p.pushMatrix() p.rotate(angle) p.line(0, 0, 0, -height) p.translate(0, -height) branches(height, angle) p.popMatrix()
def drawLEDs(network):
for n in network.G.nodes_iter():
pp.pushMatrix()
pp.noStroke()
pp.translate(*n.coords)
pp.fill(pp.color(100, 100, 100)) #all nodes are grey for now
pp.sphere(1)
pp.popMatrix()
pp.strokeWeight(3)
for e in network.G.edges_iter(data=True):
pp.pushMatrix()
pp.stroke( pp.color(*e[2]['color']) )
(x1, y1, z1) = e[0].coords
(x2, y2, z2) = e[1].coords
pp.line( x1, y1, z1, x2, y2, z2 )
pp.popMatrix()
def draw():
p5.colorMode(p5.RGB)
p5.background(0)
if len(projection):
p5.pushMatrix()
p5.colorMode(p5.HSB)
p5.translate(width/4, height/4)
p5.scale(width/2, height/2)
for point, label in zip(projection, labels):
p5.stroke(p5.color(label * 26., 255, 255))
p5.point(point[0], point[1])
p5.popMatrix()
#send osc to MaxPatch
probability_lda = model.predict_proba([getAmplitude(recent)])
send_osc_message("/lda",probability_lda)
probability_svc = clf.predict_proba([getAmplitude(recent)])
send_osc_message("/svm",probability_svc)
cur = model.transform([getAmplitude(recent)])
cur = cur[0]
cur = (cur - p_min) / (p_max - p_min)
global predicted
if predicted == None:
predicted = cur
else:
predicted = predicted * .9 + cur * .1
p5.stroke(p5.color(0, 0, 255))
p5.ellipse(width/4 + predicted[0] * width/2, height/4 + predicted[1] * height/2, 10, 10)
elif len(recent):
# draw time-amplitude
p5.pushMatrix()
p5.translate(0, height/2)
p5.scale(width / N, height/2)
p5.stroke(255)
p5.noFill()
p5.beginShape()
for x, y in enumerate(recent):
p5.vertex(x, y)
p5.endShape()
p5.popMatrix()
# draw frequency-amplitude
amp = getAmplitude(recent)
p5.pushMatrix()
p5.translate(0, height)
p5.scale(width, -height)
p5.stroke(255)
p5.noFill()
p5.beginShape()
for x, y in enumerate(amp):
p5.vertex(math.log(1+x, len(amp)), pow(y, .5))
p5.endShape()
p5.popMatrix()
def wrapped_f(): p.pushMatrix() t(*args) # perform transformation t args[-1]() # call function f p.popMatrix()
def wrapped_f(): p.pushMatrix() t(*args) # perform transformation t args[-1]() # call last argument as function p.popMatrix()
def wrapped_f(): p.pushMatrix() p.translate(0, 240) p.rotate(-math.pi / 2) f() p.popMatrix()
