text: import numpy as np
  from helpers import make_data
  from src.initialize_constraints import initialize_constraints
  import matplotlib.pyplot as plt
  from pandas import DataFrame
  import seaborn as sns
  
  def test_make_data():
  
      # your implementation of slam should work with the following inputs
      # feel free to change these input values and see how it responds!
  
      # world parameters
      num_landmarks = 5  # number of landmarks
      N = 20  # time steps
      world_size = 100.0  # size of world (square)
  
      # robot parameters
      measurement_range = 50.0  # range at which we can sense landmarks
      motion_noise = 2.0  # noise in robot motion
      measurement_noise = 2.0  # noise in the measurements
      distance = 20.0  # distance by which robot (intends to) move each iteratation
  
      # make_data instantiates a robot, AND generates random landmarks for a given world size and number of landmarks
      data = make_data(N, num_landmarks, world_size, measurement_range, motion_noise, measurement_noise, distance)
      time_step = 0
  
      print('Example measurements: \n', data[time_step][0])
      print('\n')
      print('Example motion: \n', data[time_step][1])
  
  
  def test_initialize_constraints_show_omega():
      N_test = 5
      num_landmarks_test = 2
      small_world = 10
  
      # initialize the constraints
      initial_omega, initial_xi = initialize_constraints(N_test, num_landmarks_test, small_world)
      plt.rcParams["figure.figsize"] = (10, 7)
  
      # display omega
      sns.heatmap(DataFrame(initial_omega), cmap='Blues', annot=True, linewidths=.5)
  
      plt.show()
  
  def test_initialize_constraints_show_xi():
      N_test = 5
      num_landmarks_test = 2
      small_world = 10
  
      # initialize the constraints
      initial_omega, initial_xi = initialize_constraints(N_test, num_landmarks_test, small_world)
  
      plt.rcParams["figure.figsize"] = (1, 7)
  
      # display xi
      sns.heatmap(DataFrame(initial_xi), cmap='Oranges', annot=True, linewidths=.5)
      plt.show()
  