code: def testRegression(self):
      input = essentia.array(
          np.sin(2 * np.pi * 1000 * np.arange(2048) / 44100)
      )
      # Compared against the implementation of the MATLAB CQT_toolbox_2013
      expected = np.array(
          [
              0.01764389 + 8.19244758e-06j,
              -0.00327444 + 1.78957267e-03j,
              -0.00379942 + 1.00535053e-02j,
              0.00479218 + 8.65996905e-03j,
              0.00636455 - 1.14715385e-03j,
              -0.00165716 - 6.73704576e-03j,
              -0.00948407 + 1.39929814e-03j,
              -0.00132517 + 9.10799044e-03j,
              0.00804364 + 2.70849478e-03j,
              0.00373902 - 8.13302867e-03j,
              -0.00733613 - 6.00933843e-03j,
              -0.00738841 + 5.56821084e-03j,
              0.00371405 + 8.43253605e-03j,
              0.00904939 - 1.72925594e-03j,
              0.00034281 - 9.21268760e-03j,
              -0.00891524 - 2.47832619e-03j,
              -0.00459810 + 8.25670810e-03j,
              0.00651840 + 6.09559784e-03j,
              0.00661061 - 5.63534139e-03j,
              -0.00441447 - 8.19178966e-03j,
              -0.00905809 + 1.89702405e-03j,
              0.00139695 + 6.62663074e-03j,
              0.00708779 - 1.61311132e-03j,
              0.00229181 - 9.95998412e-03j,
              -0.00574295 - 7.79506339e-03j,
              -0.00166257 + 5.33548630e-04j,
          ]
      )
  
      output = np.mean(
          self.initNsgconstantq(normalize="sine")(input)[0], axis=0
      )
  
      self.assertAlmostEqualVector(
          np.abs(expected), np.abs(output), 1e-6
      )
  
file_path: essentia/test/src/unittests/standard/test_nsgconstantq.py
imports: from essentia_test import *
  import numpy
label_desc: Use image node_id 2 for calling the TestNSGConstantQ obj's underlying member method code with example usage: obj.testRegression() without return types