code: def test_get_consistent_numpy_entries():
      """Test extraction of number of entries
  
      When first making bin_edges of input histograms consistent to each other.
      """
      df1 = pd.DataFrame(
          {
              "A": [0, 1, 2, 3, 4, 3, 2, 1, 1, 1],
              "C": [
                  "f1",
                  "f3",
                  "f4",
                  "f3",
                  "f4",
                  "f2",
                  "f2",
                  "f1",
                  "f3",
                  "f4",
              ],
          }
      )
      df2 = pd.DataFrame(
          {
              "A": [2, 3, 4, 5, 7, 4, 6, 5, 7, 8],
              "C": [
                  "f7",
                  "f3",
                  "f5",
                  "f8",
                  "f9",
                  "f2",
                  "f3",
                  "f6",
                  "f7",
                  "f7",
              ],
          }
      )
  
      # building 1d-, 2d-, and 3d-histogram (iteratively)
      hist0 = hg.Categorize(unit("C"))
      hist1 = hg.Categorize(unit("C"))
      hist2 = hg.SparselyBin(
          origin=0.0, binWidth=1.0, quantity=unit("A")
      )
      hist3 = hg.SparselyBin(
          origin=0.0, binWidth=1.0, quantity=unit("A")
      )
  
      # fill them
      for hist, df in zip(
          [hist0, hist1, hist2, hist3], [df1, df2, df1, df2]
      ):
          hist.fill.numpy(df)
  
      e0, e1 = get_consistent_numpy_entries(
          [hist0, hist1], get_bin_labels=False
      )
      _, labels01 = get_consistent_numpy_entries(
          [hist0, hist1], get_bin_labels=True
      )
  
      e2, e3 = get_consistent_numpy_entries(
          [hist2, hist3], get_bin_labels=False
      )
      _, centers23 = get_consistent_numpy_entries(
          [hist2, hist3], get_bin_labels=True
      )
  
      entries0 = [2.0, 2.0, 3.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0]
      entries1 = [0.0, 1.0, 2.0, 0.0, 1.0, 1.0, 3.0, 1.0, 1.0]
      labels = ["f1", "f2", "f3", "f4", "f5", "f6", "f7", "f8", "f9"]
  
      entries2 = [1.0, 4.0, 2.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0]
      entries3 = [0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0]
      centers = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5]
  
      np.testing.assert_array_equal(e0, entries0)
      np.testing.assert_array_equal(e1, entries1)
      np.testing.assert_array_equal(labels01, labels)
  
      np.testing.assert_array_equal(e2, entries2)
      np.testing.assert_array_equal(e3, entries3)
      np.testing.assert_array_equal(centers23, centers)
  
file_path: popmon/tests/popmon/analysis/test_hist_numpy.py
imports: import histogrammar
  import numpy
  import pandas
  import pytest
  from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists, get_2dgrid, get_consistent_numpy_1dhists, get_consistent_numpy_2dgrids, get_consistent_numpy_entries, get_contentType, prepare_2dgrid, set_2dgrid
label_desc: Use image node_id 12 for calling a global function with example usage: test_get_consistent_numpy_entries() without return types