old_contents: import numpy as np
  from numpy.testing import assert_array_almost_equal as assert_close
  
  from skimage.feature import peak
  
  
  def test_noisy_peaks():
      peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
  
      # image with noise of amplitude 0.8 and peaks of amplitude 1
      image = 0.8 * np.random.random((20, 20))
      for r, c in peak_locations:
          image[r, c] = 1
  
      peaks_detected = peak.peak_local_max(image, min_distance=5)
  
      assert len(peaks_detected) == len(peak_locations)
      for loc in peaks_detected:
          assert tuple(loc) in peak_locations
  
  
  def test_relative_threshold():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1, 1] = 10
      image[3, 3] = 20
      peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5)
      assert len(peaks) == 1
      assert_close(peaks, [(3, 3)])
  
  
  def test_absolute_threshold():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1, 1] = 10
      image[3, 3] = 20
      peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
      assert len(peaks) == 1
      assert_close(peaks, [(3, 3)])
  
  
  def test_constant_image():
      image = 128 * np.ones((20, 20), dtype=np.uint8)
      peaks = peak.peak_local_max(image, min_distance=1)
      assert len(peaks) == 0
  
  
  def test_flat_peak():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1:3, 1:3] = 10
      peaks = peak.peak_local_max(image, min_distance=1)
      assert len(peaks) == 4
  
  
  def test_num_peaks():
      image = np.zeros((3, 7), dtype=np.uint8)
      image[1, 1] = 10
      image[1, 3] = 11
      image[1, 5] = 12
      assert len(peak.peak_local_max(image, min_distance=1)) == 3
      peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
      assert len(peaks_limited) == 2
      assert (1, 3) in peaks_limited
      assert (1, 5) in peaks_limited
  
  
  if __name__ == '__main__':
      from numpy import testing
      testing.run_module_suite()
  
new_contents: import numpy as np
  from numpy.testing import assert_array_almost_equal as assert_close
  
  from skimage.feature import peak
  
  
  def test_noisy_peaks():
      peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
  
      # image with noise of amplitude 0.8 and peaks of amplitude 1
      image = 0.8 * np.random.random((20, 20))
      for r, c in peak_locations:
          image[r, c] = 1
  
      peaks_detected = peak.peak_local_max(image, min_distance=5)
  
      assert len(peaks_detected) == len(peak_locations)
      for loc in peaks_detected:
          assert tuple(loc) in peak_locations
  
  
  def test_relative_threshold():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1, 1] = 10
      image[3, 3] = 20
      peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5)
      assert len(peaks) == 1
      assert_close(peaks, [(3, 3)])
  
  
  def test_absolute_threshold():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1, 1] = 10
      image[3, 3] = 20
      peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
      assert len(peaks) == 1
      assert_close(peaks, [(3, 3)])
  
  
  def test_constant_image():
      image = 128 * np.ones((20, 20), dtype=np.uint8)
      peaks = peak.peak_local_max(image, min_distance=1)
      assert len(peaks) == 0
  
  
  def test_flat_peak():
      image = np.zeros((5, 5), dtype=np.uint8)
      image[1:3, 1:3] = 10
      peaks = peak.peak_local_max(image, min_distance=1)
      assert len(peaks) == 4
  
  
  def test_num_peaks():
      image = np.zeros((7, 7), dtype=np.uint8)
      image[1, 1] = 10
      image[1, 3] = 11
      image[1, 5] = 12
      image[3, 5] = 8
      image[5, 3] = 7
      assert len(peak.peak_local_max(image, min_distance=1)) == 5
      peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
      assert len(peaks_limited) == 2
      assert (1, 3) in peaks_limited
      assert (1, 5) in peaks_limited
      peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=4)
      assert len(peaks_limited) == 4
      assert (1, 3) in peaks_limited
      assert (1, 5) in peaks_limited
      assert (1, 1) in peaks_limited
      assert (3, 5) in peaks_limited
  
  
  if __name__ == '__main__':
      from numpy import testing
      testing.run_module_suite()
  
subject: extend test cases for num_peaks parameter of peak_local_max