docstr: Test conversion of sparse to 2D dense with no overlapping labels
  
  dense output: label=1, c=1,t=1,z=1,y=3,x=3
  
      1 1 0
      2 0 0
      2 0 0
docstr_tok: ["Test","conversion","of","sparse","to","2D","dense","with","no","overlapping","labels","dense","output",":","label=1",",","c=1",",","t=1",",","z=1",",","y=3",",","x=3","1","1","0","2","0","0","2","0","0"]
code: def test_02_02_sparse_to_2d_dense_nooverlap(self):
      """
      Test conversion of sparse to 2D dense with no overlapping labels
  
      dense output: label=1, c=1,t=1,z=1,y=3,x=3
  
          1 1 0
          2 0 0
          2 0 0
      """
      sparse = np.array(
          [(0, 0, 1), (0, 1, 1), (1, 0, 2), (2, 0, 2)],
          dtype=[
              (SPARSE_FIELD.y.value, "<u2"),
              (SPARSE_FIELD.x.value, "<u2"),
              (SPARSE_FIELD.label.value, "u1"),
          ],
      )
  
      dense_plain, dpi = lib_seg.convert_sparse_to_dense(sparse)
      dense_shaped, dsi = lib_seg.convert_sparse_to_dense(
          sparse, dense_shape=(1, 1, 1, 3, 3)
      )
  
      # + 1 padding
      dense_plain_expected = np.array(
          [1, 1, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0], np.uint8
      ).reshape((1, 1, 1, 1, 4, 3))
      dense_shaped_expected = np.array(
          [1, 1, 0, 2, 0, 0, 2, 0, 0], np.uint8
      ).reshape((1, 1, 1, 1, 3, 3))
      indices_expected = [np.array([1, 2], np.uint8)]
  
      np.testing.assert_array_equal(dense_plain, dense_plain_expected)
      np.testing.assert_array_equal(dense_shaped, dense_shaped_expected)
      _check_indices(dpi, indices_expected)
      _check_indices(dsi, indices_expected)
  
imports: import numpy
  import centrosome.outline
  import cellprofiler_library.functions.segmentation
  from cellprofiler_library.functions.segmentation import SPARSE_FIELD
label_desc: Use image node_id 9 for calling the TestSegmentation obj's underlying member method code with example usage: obj.test_02_02_sparse_to_2d_dense_nooverlap() without return types