message: completed as...
diff: @@ -30,7 +30,9 @@ def _build_dist_matrix(X, Y, order=2., internal_p=2.):
  :param order: exponent for the Wasserstein metric.
  :param internal_p: Ground metric (i.e. norm L^p).
  :returns: (n+1) x (m+1) np.array encoding the cost matrix C.
  - For 1 <= i <= n, 1 <= j <= m, C[i,j] encodes the distance between X[i] and Y[j], while C[i, m+1] (resp. C[n+1, j]) encodes the distance (to the p) between X[i] (resp Y[j]) and its orthogonal proj onto the diagonal.
  + For 1 <= i <= n, 1 <= j <= m, C[i,j] encodes the distance between X[i] and Y[j],
  + while C[i, m+1] (resp. C[n+1, j]) encodes the distance (to the p) between X[i] (resp Y[j])
  + and its orthogonal proj onto the diagonal.
  note also that C[n+1, m+1] = 0 (it costs nothing to move from the diagonal to the diagonal).
  '''
  Xdiag = _proj_on_diag(X)
  @@ -88,7 +90,9 @@ def wasserstein_distance(X, Y, matching=False, order=2., internal_p=2.):
  :param X: (n x 2) numpy.array encoding the (finite points of the) first diagram. Must not contain essential points
  (i.e. with infinite coordinate).
  :param Y: (m x 2) numpy.array encoding the second diagram.
  - :param matching: if True, computes and returns the optimal matching between X and Y, encoded as...
  + :param matching: if True, computes and returns the optimal matching between X and Y, encoded as
  + a list of tuple [...(i,j)...], meaning the i-th point in X is matched to
  + the j-th point in Y, with the convention (-1) represents the diagonal.
  :param order: exponent for Wasserstein; Default value is 2.
  :param internal_p: Ground metric on the (upper-half) plane (i.e. norm L^p in R^2);
  Default value is 2 (Euclidean norm).
  