bolt/spark/array.py
def _stat(self, axis=None, func=None, name=None, keepdims=False):
        """
        Compute a statistic over an axis.

        Can provide either a function (for use in a reduce)
        or a name (for use by a stat counter).

        Parameters
        ----------
        axis : tuple or int, optional, default=None
            Axis to compute statistic over, if None
            will compute over all axes

        func : function, optional, default=None
            Function for reduce, see BoltArraySpark.reduce

        name : str
            A named statistic, see StatCounter

        keepdims : boolean, optional, default=False
            Keep axis remaining after operation with size 1.
        """
        if axis is None:
            axis = list(range(len(self.shape)))
        axis = tupleize(axis)

        if func and not name:
            return self.reduce(func, axis, keepdims)

        if name and not func:
            from bolt.local.array import BoltArrayLocal

            swapped = self._align(axis)

            def reducer(left, right):
                return left.combine(right)

            counter = swapped._rdd.values()\
                             .mapPartitions(lambda i: [StatCounter(values=i, stats=name)])\
                             .treeReduce(reducer, depth=3)

            arr = getattr(counter, name)

            if keepdims:
                for i in axis:
                    arr = expand_dims(arr, axis=i)

            return BoltArrayLocal(arr).toscalar()

        else:
            raise ValueError('Must specify either a function or a statistic name.')
def _stat(self, axis=None, func=None, name=None, keepdims=False):
        if axis is None:
            axis = list(range(len(self.shape)))
        axis = tupleize(axis)

        if func and not name:
            return self.reduce(func, axis, keepdims)

        if name and not func:
            from bolt.local.array import BoltArrayLocal

            swapped = self._align(axis)

            def reducer(left, right):
                return left.combine(right)

            counter = swapped._rdd.values()\
                             .mapPartitions(lambda i: [StatCounter(values=i, stats=name)])\
                             .treeReduce(reducer, depth=3)

            arr = getattr(counter, name)

            if keepdims:
                for i in axis:
                    arr = expand_dims(arr, axis=i)

            return BoltArrayLocal(arr).toscalar()

        else:
            raise ValueError('Must specify either a function or a statistic name.')
def _stat ( self , axis = None , func = None , name = None , keepdims = False ) : if axis is None : axis = list ( range ( len ( self . shape ) ) ) axis = tupleize ( axis ) if func and not name : return self . reduce ( func , axis , keepdims ) if name and not func : from bolt . local . array import BoltArrayLocal swapped = self . _align ( axis ) def reducer ( left , right ) : return left . combine ( right ) counter = swapped . _rdd . values ( ) . mapPartitions ( lambda i : [ StatCounter ( values = i , stats = name ) ] ) . treeReduce ( reducer , depth = 3 ) arr = getattr ( counter , name ) if keepdims : for i in axis : arr = expand_dims ( arr , axis = i ) return BoltArrayLocal ( arr ) . toscalar ( ) else : raise ValueError ( 'Must specify either a function or a statistic name.' )
Compute a statistic over an axis.

        Can provide either a function (for use in a reduce)
        or a name (for use by a stat counter).

        Parameters
        ----------
        axis : tuple or int, optional, default=None
            Axis to compute statistic over, if None
            will compute over all axes

        func : function, optional, default=None
            Function for reduce, see BoltArraySpark.reduce

        name : str
            A named statistic, see StatCounter

        keepdims : boolean, optional, default=False
            Keep axis remaining after operation with size 1.
Compute a statistic over an axis .