Function min

Wrapping/Python/vtkmodules/numpy_interface/numpy_algorithms.py:232–269

Returns the min of all values along a particular axis (dimension). Given an array of m tuples and n components: * Default is to return the min of all values in an array. * axis=0: Return the min values of all tuples and return a one tuple, n-component array. * axis=1: Return th

(array, axis=None, controller=None)

@dsa._override_numpy(numpy.min)
def min(array, axis=None, controller=None):
    """Returns the min of all values along a particular axis (dimension).
    Given an array of m tuples and n components:
    * Default is to return the min of all values in an array.
    * axis=0: Return the min values of all tuples and return a one
      tuple, n-component array.
    * axis=1: Return the min values of all components of each tuple and
      return an m-tuple, 1-component array.

    When called in parallel, this function will compute the min across processes
    when a controller argument is passed or the global controller is defined.
    To disable parallel summing when running in parallel, pass a dummy controller as follows:

    min(array, controller=vtkmodules.vtkParallelCore.vtkDummyController()).
    """
    from .algorithms import global_func
    class MinImpl:
        def op(self):
            return algs.min

        def mpi_op(self):
            from mpi4py import MPI
            return MPI.MIN

        def serial_composite(self, array, axis):
            res = _apply_func2(algs.min, array, (axis,))
            clean_list = []
            for a in res:
                if a is not dsa.NoneArray:
                    clean_list.append(a)
            if clean_list is []:
                return None
            return algs.min(clean_list, axis=0).astype(numpy.float64)

        def default(self):
            return numpy.finfo(numpy.float64).max

    return global_func(MinImpl(), array, axis, controller)

@dsa._override_numpy(numpy.all)
def all(array, axis=None, controller=None):