old_contents: from mousestyles.distribution import (powerlaw_pdf, exp_pdf,
                                        powerlaw_inverse_cdf, exp_inverse_cdf)
  
  
  def test_powerlaw_pdf():
      assert (powerlaw_pdf(2, 2) == 0.25)
  
  
  def test_exp_pdf():
      assert (exp_pdf(2, 1) == 0.36787944117144233)
  
  
  def test_powerlaw_inverse_cdf():
      y = np.arange(0, 1, 0.01)
      x = powerlaw_inverse_cdf(y, 2)
      assert (type(x) == np.ndarray)
      assert (all([item >= 1 for item in x]))
      assert (x[0] == 1)
  
  
  def test_exp_inverse_cdf():
      y = np.arange(0, 1, 0.01)
      x = exp_inverse_cdf(y, 2)
      assert (type(x) == np.ndarray)
      assert (all([item >= 1 for item in x]))
      assert (x[0] == 1)
  
new_contents: import numpy as np
  from mousestyles.distribution import (powerlaw_pdf, exp_pdf,
                                        powerlaw_inverse_cdf, exp_inverse_cdf)
  
  
  def test_powerlaw_pdf():
      assert (powerlaw_pdf(2, 2) == 0.25)
  
  
  def test_exp_pdf():
      assert (exp_pdf(2, 1) == 0.36787944117144233)
  
  
  def test_powerlaw_inverse_cdf():
      y = np.arange(0, 1, 0.01)
      x = powerlaw_inverse_cdf(y, 2)
      assert (type(x) == np.ndarray)
      assert (all([item >= 1 for item in x]))
      assert (x[0] == 1)
  
  
  def test_exp_inverse_cdf():
      y = np.arange(0, 1, 0.01)
      x = exp_inverse_cdf(y, 2)
      assert (type(x) == np.ndarray)
      assert (all([item >= 1 for item in x]))
      assert (x[0] == 1)
  
subject: modify the code style