time_alignment_plotting_tools.py 文件源码

def plot_results(times_A, times_B, signal_A, signal_B,
                 convoluted_signals, time_offset, block=True):

  fig = plt.figure()

  title_position = 1.05

  matplotlib.rcParams.update({'font.size': 20})

  # fig.suptitle("Time Alignment", fontsize='24')
  a1 = plt.subplot(1, 3, 1)

  a1.get_xaxis().get_major_formatter().set_useOffset(False)

  plt.ylabel('angular velocity norm [rad]')
  plt.xlabel('time [s]')
  a1.set_title(
      "Before Time Alignment", y=title_position)
  plt.hold("on")

  min_time = min(np.amin(times_A), np.amin(times_B))
  times_A_zeroed = times_A - min_time
  times_B_zeroed = times_B - min_time

  plt.plot(times_A_zeroed, signal_A, c='r')
  plt.plot(times_B_zeroed, signal_B, c='b')

  times_A_shifted = times_A + time_offset

  a3 = plt.subplot(1, 3, 2)
  a3.get_xaxis().get_major_formatter().set_useOffset(False)
  plt.ylabel('correlation')
  plt.xlabel('sample idx offset')
  a3.set_title(
      "Correlation Result \n[Ideally has a single dominant peak.]",
      y=title_position)
  plt.hold("on")
  plt.plot(np.arange(-len(signal_A) + 1, len(signal_B)), convoluted_signals)

  a2 = plt.subplot(1, 3, 3)
  a2.get_xaxis().get_major_formatter().set_useOffset(False)
  plt.ylabel('angular velocity norm [rad]')
  plt.xlabel('time [s]')
  a2.set_title(
      "After Time Alignment", y=title_position)
  plt.hold("on")
  min_time = min(np.amin(times_A_shifted), np.amin(times_B))
  times_A_shifted_zeroed = times_A_shifted - min_time
  times_B_zeroed = times_B - min_time
  plt.plot(times_A_shifted_zeroed, signal_A, c='r')
  plt.plot(times_B_zeroed, signal_B, c='b')

  plt.subplots_adjust(left=0.04, right=0.99, top=0.8, bottom=0.15)

  if plt.get_backend() == 'TkAgg':
    mng = plt.get_current_fig_manager()
    max_size = mng.window.maxsize()
    max_size = (max_size[0], max_size[1] * 0.45)
    mng.resize(*max_size)
  plt.show(block=block)