How to use the mc3.stats.bin_array function in mc3

def test_bin_array_unweighted():
    data = np.array([0,1,2, 3,3,3, 3,3,4])
    binsize = 3
    bindata = ms.bin_array(data, binsize)
    np.testing.assert_allclose(bindata,
        np.array([1.0, 3.0, np.mean([3,3,4])]))

def test_bin_array_weighted():
    data = np.array([0,1,2, 3,3,3, 3,3,4])
    unc  = np.array([3,1,1, 1,2,3, 2,2,4])
    binsize = 3
    bindata, binstd = ms.bin_array(data, binsize, unc)
    np.testing.assert_allclose(bindata,
        np.array([1.42105263, 3.0, 3.11111111]))
    np.testing.assert_allclose(binstd,
        np.array([0.68824720, 0.85714286, 1.33333333]))

fignum: Integer
      The figure number.
  savefile: Boolean
      If not None, name of file to save the plot.
  fmt: String
      Format of the plotted markers.

  Returns
  -------
  ax: matplotlib.axes.Axes
      Axes instance containing the marginal posterior distributions.
  """
  # Bin down array:
  binsize = int((np.size(data)-1)/nbins + 1)
  binindp  = ms.bin_array(indparams, binsize)
  binmodel = ms.bin_array(model,     binsize)
  bindata, binuncert = ms.bin_array(data, binsize, uncert)
  fs = 12 # Font-size

  plt.figure(fignum, figsize=(8,6))
  plt.clf()

  # Residuals:
  rax = plt.axes([0.15, 0.1, 0.8, 0.2])
  rax.errorbar(binindp, bindata-binmodel, binuncert, fmt='ko', ms=4)
  rax.plot([indparams[0], indparams[-1]], [0,0],'k:',lw=1.5)
  rax.tick_params(labelsize=fs-1)
  rax.set_xlabel("x", fontsize=fs)
  rax.set_ylabel('Residuals', fontsize=fs)

  # Data and Model:
  ax = plt.axes([0.15, 0.35, 0.8, 0.55])

The figure number.
  savefile: Boolean
      If not None, name of file to save the plot.
  fmt: String
      Format of the plotted markers.

  Returns
  -------
  ax: matplotlib.axes.Axes
      Axes instance containing the marginal posterior distributions.
  """
  # Bin down array:
  binsize = int((np.size(data)-1)/nbins + 1)
  binindp  = ms.bin_array(indparams, binsize)
  binmodel = ms.bin_array(model,     binsize)
  bindata, binuncert = ms.bin_array(data, binsize, uncert)
  fs = 12 # Font-size

  plt.figure(fignum, figsize=(8,6))
  plt.clf()

  # Residuals:
  rax = plt.axes([0.15, 0.1, 0.8, 0.2])
  rax.errorbar(binindp, bindata-binmodel, binuncert, fmt='ko', ms=4)
  rax.plot([indparams[0], indparams[-1]], [0,0],'k:',lw=1.5)
  rax.tick_params(labelsize=fs-1)
  rax.set_xlabel("x", fontsize=fs)
  rax.set_ylabel('Residuals', fontsize=fs)

  # Data and Model:
  ax = plt.axes([0.15, 0.35, 0.8, 0.55])
  ax.errorbar(binindp, bindata, binuncert, fmt='ko', ms=4, label='Binned Data')

Number of bins in the output plot.
  fignum: Integer
      The figure number.
  savefile: Boolean
      If not None, name of file to save the plot.
  fmt: String
      Format of the plotted markers.

  Returns
  -------
  ax: matplotlib.axes.Axes
      Axes instance containing the marginal posterior distributions.
  """
  # Bin down array:
  binsize = int((np.size(data)-1)/nbins + 1)
  binindp  = ms.bin_array(indparams, binsize)
  binmodel = ms.bin_array(model,     binsize)
  bindata, binuncert = ms.bin_array(data, binsize, uncert)
  fs = 12 # Font-size

  plt.figure(fignum, figsize=(8,6))
  plt.clf()

  # Residuals:
  rax = plt.axes([0.15, 0.1, 0.8, 0.2])
  rax.errorbar(binindp, bindata-binmodel, binuncert, fmt='ko', ms=4)
  rax.plot([indparams[0], indparams[-1]], [0,0],'k:',lw=1.5)
  rax.tick_params(labelsize=fs-1)
  rax.set_xlabel("x", fontsize=fs)
  rax.set_ylabel('Residuals', fontsize=fs)

  # Data and Model: