andes.core.service.NumReduce

class andes.core.service.NumReduce(u, ref: BackRef, fun: Callable, name=None, tex_name=None, info=None, cache=True)

A helper Service type which reduces a linearly stored 2-D ExtParam into 1-D Service.

NumReduce works with ExtParam whose v field is a list of lists. A reduce function which takes an array-like and returns a scalar need to be supplied. NumReduce calls the reduce function on each of the lists and return all the scalars in an array.

Parameters:

uExtParam

Input ExtParam whose v contains linearly stored 2-dimensional values

refBackRef

The BackRef whose 2-dimensional shapes are used for indexing

funCallable

The callable for converting a 1-D array-like to a scalar

Examples

Suppose one wants to calculate the mean value of the Vn in one Area. In the Area class, one defines

class AreaModel(...):
    def __init__(...):
        ...
        # backward reference from `Bus`
        self.Bus = BackRef()

        # collect the Vn in an 1-D array
        self.Vn = ExtParam(model='Bus',
                           src='Vn',
                           indexer=self.Bus)

        self.Vn_mean = NumReduce(u=self.Vn,
                                 fun=np.mean,
                                 ref=self.Bus)

Suppose we define two areas, 1 and 2, the Bus data looks like

idx	area	Vn
1	1	110
2	2	220
3	1	345
4	1	500

Then, self.Bus.v is a list of two lists [ [1, 3, 4], [2] ]. self.Vn.v will be retrieved and linearly stored as [110, 345, 500, 220]. Based on the shape from self.Bus, numpy.mean() will be called on [110, 345, 500] and [220] respectively. Thus, self.Vn_mean.v will become [318.33, 220].

__init__(u, ref: BackRef, fun: Callable, name=None, tex_name=None, info=None, cache=True)

Methods

assign_memory(n)	Assign memory for self.v and set the array to zero.
get_names()	Return name in a list

Attributes

class_name	Return the class name
n	Return the count of values in self.v.
v	Return the reduced values from the reduction function in an array