class_docstr: The contrast class handles the estimation of statistical contrasts
  on a given model: student (t), Fisher (F), conjunction (tmin-conjunction).
  The important feature is that it supports addition,
  thus opening the possibility of fixed-effects models.
  
  The current implementation is meant to be simple,
  and could be enhanced in the future on the computational side
  (high-dimensional F contrasts may lead to memory breakage).
  
  Notes
  -----
  The 'tmin-conjunction' test is the valid conjunction test discussed in:
  Nichols T, Brett M, Andersson J, Wager T, Poline JB. Valid conjunction
  inference with the minimum statistic. Neuroimage. 2005 Apr 15;25(3):653-60.
  This test gives the p-value of the z-values under the conjunction null,
  i.e. the union of the null hypotheses for all terms.
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docstr: Return the decision statistic associated with the test of the
  null hypothesis: (H0) 'contrast equals baseline'
  
  Parameters
  ==========
  baseline: float, optional,
            Baseline value for the test statistic
docstr_tok: ["Return","the","decision","statistic","associated","with","the","test","of","the","null","hypothesis",":","(","H0",")","'contrast","equals","baseline'","Parameters","==========","baseline",":","float",",","optional",",","Baseline","value","for","the","test","statistic"]
code: def stat(self, baseline=0.0):
      """Return the decision statistic associated with the test of the
      null hypothesis: (H0) 'contrast equals baseline'
  
      Parameters
      ==========
      baseline: float, optional,
                Baseline value for the test statistic
      """
      self.baseline = baseline
  
      # Case: one-dimensional contrast ==> t or t**2
      if self.dim == 1:
          # avoids division by zero
          stat = (self.effect - baseline) / np.sqrt(
              np.maximum(self.variance, self.tiny)
          )
          if self.contrast_type == "F":
              stat = stat**2
      # Case: F contrast
      elif self.contrast_type == "F":
          # F = |t|^2/q ,  |t|^2 = e^t inv(v) e
          if self.effect.ndim == 1:
              self.effect = self.effect[np.newaxis]
          if self.variance.ndim == 1:
              self.variance = self.variance[np.newaxis, np.newaxis]
          stat = (
              multiple_mahalanobis(
                  self.effect - baseline, self.variance
              )
              / self.dim
          )
      # Case: tmin (conjunctions)
      elif self.contrast_type == "tmin-conjunction":
          vdiag = self.variance.reshape(
              [self.dim**2] + list(self.variance.shape[2:])
          )[:: self.dim + 1]
          stat = (self.effect - baseline) / np.sqrt(
              np.maximum(vdiag, self.tiny)
          )
          stat = stat.min(0)
  
      # Unknown stat
      else:
          raise ValueError("Unknown statistic type")
      self.stat_ = stat
      return stat.ravel()
  
imports: from warnings import warn
  import numpy
  import scipy.stats
  from nibabel import Nifti1Image, load
  from nipy.algorithms.statistics.models.regression import ARModel, OLSModel
  from nipy.algorithms.statistics.utils import multiple_mahalanobis, z_score
  from nipy.io.nibcompat import get_affine, get_header
  from nipy.labs.mask import compute_mask_sessions
label_desc: Use image node_id 2 for calling the Contrast obj's underlying member method code with example usage: obj.stat(baseline) and returns: stat