code: def select_mask(cls, dataset, selection):
          
          mask = np.ones(len(dataset), dtype=np.bool)
          for dim, k in selection.items():
              if isinstance(k, tuple):
                  k = slice(*k)
              arr = cls.values(dataset, dim)
              if isinstance(k, slice):
                  with warnings.catch_warnings():
                      warnings.filterwarnings('ignore', r'invalid value encountered')
                      if k.start is not None:
                          mask &= k.start <= arr
                      if k.stop is not None:
                          mask &= arr < k.stop
              elif isinstance(k, (set, list)):
                  iter_slcs = []
                  for ik in k:
                      with warnings.catch_warnings():
                          warnings.filterwarnings('ignore', r'invalid value encountered')
                          iter_slcs.append(arr == ik)
                  mask &= np.logical_or.reduce(iter_slcs)
              elif callable(k):
                  mask &= k(arr)
              else:
                  index_mask = arr == k
                  if dataset.ndims == 1 and np.sum(index_mask) == 0:
                      data_index = np.argmin(np.abs(arr - k))
                      mask = np.zeros(len(dataset), dtype=np.bool)
                      mask[data_index] = True
                  else:
                      mask &= index_mask
          return mask
docs: Given a Dataset object and a dictionary with dimension keys and
          selection keys (i.e tuple ranges, slices, sets, lists or literals)
          return a boolean mask over the rows in the Dataset object that
          have been selected.