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: Addition of selfwith others, Yields an new Contrast instance
  This should be used only on indepndent contrasts
docstr_tok: ["Addition","of","selfwith","others",",","Yields","an","new","Contrast","instance","This","should","be","used","only","on","indepndent","contrasts"]
code: def __add__(self, other):
      """Addition of selfwith others, Yields an new Contrast instance
      This should be used only on indepndent contrasts"""
      if self.contrast_type != other.contrast_type:
          raise ValueError(
              "The two contrasts do not have consistent type dimensions"
          )
      if self.dim != other.dim:
          raise ValueError(
              "The two contrasts do not have compatible dimensions"
          )
      effect_ = self.effect + other.effect
      variance_ = self.variance + other.variance
      dof_ = self.dof + other.dof
      return Contrast(
          effect=effect_,
          variance=variance_,
          dof=dof_,
          contrast_type=self.contrast_type,
      )
  
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 5 for calling the Contrast obj's underlying member method code with example usage: obj.__add__(other) and returns: Contrast