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: