aggregationarg.py

class AggregationArg(object):

Aggregation model argument container

Attributes:

- operands: number of operands as integer
- target_variable: target indice tuple (level, variable)
- target_index: branch-object index that identifies the target objects
- remove_targets: objects that were missing and should be removed
- use_nan_funcs: True if operand values contain NaNs due to aggregation condition
- values: operand values as list of numpy arrays
- weights: weight values as list of numpy arrays
- oper_target_index: operand target indices as a deque of numpy arrays
- oper_data_level: operand data level indices in deque
- success: model evaluation success as boolean
- errors: list of error messages
- results: results as numpy array

def __init__(self, ind, tind, torem, scalar, deterministic=False):

>>> from simo.simulation.model.aggregationarg import AggregationArg
>>> import numpy
>>> ind = (1,1)
>>> tind = numpy.array([[0,0,0,0,0],
...                     [0,0,1,0,0],
...                     [0,0,2,0,0],
...                     [0,0,3,0,0],
...                     [0,0,4,0,0]], dtype=int)
>>> torem = set([1])
>>> arg = AggregationArg(ind, tind, torem, True)
>>> arg.target_variable
(1, 1)

def add_operand(self, datalevel, tind, rm_targets, values, weights, failed):

Add operand values and weights to argument container

Add a single operand with weight:

>>> arg.add_operand(1, tind, set([]), [1,1,1,1,1], [2,2,2,2,2], [])
>>> arg.operands
1
>>> arg.values
deque([[1, 1, 1, 1, 1]])
>>> arg.weights
deque([[2, 2, 2, 2, 2]])

Add second operand with weight:

>>> arg.add_operand(1, tind, set([3]), [1,1,1,1,1], [2,2,2,2,2], [])
>>> arg.operands
2
>>> arg.values
deque([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]])
>>> arg.weights
deque([[2, 2, 2, 2, 2], [2, 2, 2, 2, 2]])
>>> arg.oper_target_index
deque([array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 3, 0, 0],
       [0, 0, 4, 0, 0]]), array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 2, 0, 0],
       [0, 0, 3, 0, 0],
       [0, 0, 4, 0, 0]])])