class_docstr: histogram for each variable in a segmented time series
  
  .. note:: this feature is expensive to compute with the current implementation
class_docstr_tok: ["histogram","for","each","variable","in","a","segmented","time","series","..","note",":",":","this","feature","is","expensive","to","compute","with","the","current","implementation"]
code: def __call__(self, X):
      X = np.atleast_3d(X)
      N = X.shape[0]
      D = X.shape[2]
      histogram = np.zeros((N, D * self.bins))
      for i in np.arange(N):
          for j in np.arange(D):
              # for each variable, advance by bins
              histogram[
                  i, (j * self.bins) : ((j + 1) * self.bins)
              ] = np.histogram(
                  X[i, :, j], bins=self.bins, density=True
              )[
                  0
              ]
  
      return histogram
  
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 2 for calling the hist obj's underlying member method code with example usage: obj.__call__(X) and returns: histogram