def animate(i):
analog_data=parser.readLidar()
for index, item in enumerate(analog_data):
x,y = pol2cart(item,x_data[index])
if abs(x) > 2000:
x=0
if abs(y) > 2000:
y=0
ptArray[x+2000][y+2000]=200
lines[0].set_data(x_data, analog_data)
'''print np.sum(ptArray)'''
return tuple(lines)
def animate(i):
analog_data = parser.readLidar()
for index, item in enumerate(analog_data):
x, y = pol2cart(item, x_data[index])
if abs(x) > 2000:
x = 0
if abs(y) > 2000:
y = 0
ptArray[x + 2000][y + 2000] = 200
lines[0].set_data(x_data, analog_data)
'''print np.sum(ptArray)'''
return tuple(lines)
def animate(i):
global start, last_x
newnow = datetime.datetime.now();
delta = newnow - start;
start = newnow
seconds = delta.total_seconds();
#print("time since last animate = ", seconds)
samples = round(seconds * 5000)
x = np.linspace(last_x, last_x + seconds, samples)
last_x += seconds;
analog_data[0]=parser.readLidar()
analog_data[1].extend(np.add(np.multiply(np.abs(np.sin(2 * np.pi * x * -1.0)), .3), 1.3))
lines[0].set_data(x_data, analog_data[0])
return tuple(lines)
def animate(i):
global start, last_x
newnow = datetime.datetime.now()
delta = newnow - start
start = newnow
seconds = delta.total_seconds()
#print("time since last animate = ", seconds)
samples = round(seconds * 5000)
x = np.linspace(last_x, last_x + seconds, samples)
last_x += seconds
analog_data[0] = parser.readLidar()
analog_data[1].extend(
np.add(np.multiply(np.abs(np.sin(2 * np.pi * x * -1.0)), .3), 1.3))
lines[0].set_data(x_data, analog_data[0])
return tuple(lines)
'''print np.sum(ptArray)'''
return tuple(lines)
# call the animator. blit=True means only re-draw the parts that have changed.
'''anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=1, interval=0, blit=True)'''
plt.xlabel('Time (s)')
plt.ylabel('Voltage (v)')
plt.ion()
plt.show()
for i in range(90):
print i
animate(i)
plt.draw()
parser.lidarOff()
# save the animation as an mp4. This requires ffmpeg or mencoder to be
# installed. The extra_args ensure that the x264 codec is used, so that
# the video can be embedded in html5. You may need to adjust this for
# your system: for more information, see
# http://matplotlib.sourceforge.net/api/animation_api.html
#anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
plt.grid(True)
print '4'
Houghlines = cv2.HoughLines(ptArray, 1, np.pi / 180, 10)
print '5'
print Houghlines
print '3'
for i in Houghlines:
import NewParser
if __name__ == "__main__":
f = open('Tests/Test_Cases/SimpleSmaliTest/MainActivity.smali', 'r')
smali_class = NewParser.parseSmaliFiles(f)
print(smali_class.classname)
for f in smali_class.fields:
print(f.name, f.value)
return tuple(lines)
# call the animator. blit=True means only re-draw the parts that have changed.
'''anim = animation.FuncAnimation(fig, animate, init_func=init,
frames=1, interval=0, blit=True)'''
plt.xlabel('Time (s)')
plt.ylabel('Voltage (v)')
plt.ion()
plt.show()
for i in range(90):
print i
animate(i)
plt.draw()
parser.lidarOff()
# save the animation as an mp4. This requires ffmpeg or mencoder to be
# installed. The extra_args ensure that the x264 codec is used, so that
# the video can be embedded in html5. You may need to adjust this for
# your system: for more information, see
# http://matplotlib.sourceforge.net/api/animation_api.html
#anim.save('basic_animation.mp4', fps=30, extra_args=['-vcodec', 'libx264'])
plt.grid(True)
print '4'
Houghlines = cv2.HoughLines(ptArray,1,np.pi/180,10)
print '5'
print Houghlines
print '3'
for i in Houghlines:
