Source code for openmdao.components.add_subtract_comp

"""
Definition of the Add/Subtract Component.
"""
import collections
from six import string_types

import numpy as np
from scipy import sparse as sp

from openmdao.core.explicitcomponent import ExplicitComponent


[docs]class AddSubtractComp(ExplicitComponent): r""" Compute a vectorized element-wise addition or subtraction. Use the add_equation method to define any number of add/subtract relations User defines the names of the input and output variables using add_equation(output_name='my_output', input_names=['a','b', 'c', ...]) .. math:: result = a * \textrm{scaling factor}_a + b * \textrm{scaling factor}_b + c * \textrm{scaling factor}_c + ... where all inputs shape (vec_size, n) b is of shape (vec_size, n) c is of shape (vec_size, n) result is of shape (vec_size, n) All input vectors must be of the same shape, specified by the options 'vec_size' and 'length'. Use scaling factor -1 for subtraction. Attributes ---------- _add_systems : list List of equation systems to be initialized with the system. """
[docs] def __init__(self, output_name=None, input_names=None, vec_size=1, length=1, val=1.0, scaling_factors=None, **kwargs): """ Allow user to create an addition/subtracton system with one-liner. Parameters ---------- output_name : str (required) name of the result variable in this component's namespace. input_names : iterable of str (required) names of the input variables for this system vec_size : int Length of the first dimension of the input and output vectors (i.e number of rows, or vector length for a 1D vector) Default is 1 length : int Length of the second dimension of the input and ouptut vectors (i.e. number of columns) Default is 1 which results in input/output vectors of size (vec_size,) scaling_factors : iterable of numeric Scaling factors to apply to each input. Use [1,1,...] for addition, [1,-1,...] for subtraction Must be same length as input_names Default is None which results in a scaling factor of 1 on each input (element-wise addition) val : float or list or tuple or ndarray The initial value of the variable being added in user-defined units. Default is 1.0. **kwargs : str Any other arguments to pass to the addition system (same as add_output method for ExplicitComponent) Examples include units (str or None), desc (str) """ super(AddSubtractComp, self).__init__() self._add_systems = [] if isinstance(output_name, string_types): self._add_systems.append((output_name, input_names, vec_size, length, val, scaling_factors, kwargs)) elif isinstance(output_name, collections.Iterable): raise NotImplementedError(self.msginfo + ': Declaring multiple addition systems ' 'on initiation is not implemented.' 'Use a string to name a single addition relationship or use ' 'multiple add_output calls') elif output_name is None: pass else: raise ValueError(self.msginfo + ": first argument to adder init must be either of " "type `str' or 'None'")
[docs] def initialize(self): """ Declare options. Parameters ---------- complex : Boolean Set True to enable complex math (e.g. for complex step verification) """ self.options.declare('complex', types=bool, default=False, desc="Allocate as complex (e.g. for complex-step verification)")
[docs] def add_equation(self, output_name, input_names, vec_size=1, length=1, val=1.0, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=None, scaling_factors=None): """ Add an addition/subtraction relation. Parameters ---------- output_name : str (required) name of the result variable in this component's namespace. input_names : iterable of str (required) names of the input variables for this system vec_size : int Length of the first dimension of the input and output vectors (i.e number of rows, or vector length for a 1D vector) Default is 1 length : int Length of the second dimension of the input and ouptut vectors (i.e. number of columns) Default is 1 which results in input/output vectors of size (vec_size,) scaling_factors : iterable of numeric Scaling factors to apply to each input. Use [1,1,...] for addition, [1,-1,...] for subtraction Must be same length as input_names Default is None which results in a scaling factor of 1 on each input (element-wise addition) val : float or list or tuple or ndarray The initial value of the variable being added in user-defined units. Default is 1.0. units : str or None Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units. res_units : str or None Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units. desc : str description of the variable. lower : float or list or tuple or ndarray or Iterable or None lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None. upper : float or list or tuple or ndarray or or Iterable None upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None. ref : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1. ref0 : float or ndarray Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0. res_ref : float or ndarray Scaling parameter. The value in the user-defined res_units of this output's residual when the scaled value is 1. Default is 1. """ kwargs = {'units': units, 'res_units': res_units, 'desc': desc, 'lower': lower, 'upper': upper, 'ref': ref, 'ref0': ref0, 'res_ref': res_ref} self._add_systems.append((output_name, input_names, vec_size, length, val, scaling_factors, kwargs))
[docs] def add_output(self): """ Use add_equation instead of add_output to define equation systems. """ raise NotImplementedError(self.msginfo + ': Use add_equation method, not add_output ' 'method to create an addition/subtraction relation')
[docs] def setup(self): """ Set up the addition/subtraction system at run time. """ for (output_name, input_names, vec_size, length, val, scaling_factors, kwargs) in self._add_systems: if isinstance(input_names, string_types): input_names = [input_names] units = kwargs['units'] desc = kwargs['desc'] if scaling_factors is None: scaling_factors = np.ones(len(input_names)) if len(scaling_factors) != len(input_names): raise ValueError(self.msginfo + ': Scaling factors list needs to be same length ' 'as input names') if length == 1: shape = (vec_size,) else: shape = (vec_size, length) super(AddSubtractComp, self).add_output(output_name, val, shape=shape, **kwargs) for i, input_name in enumerate(input_names): self.add_input(input_name, shape=shape, units=units, desc=desc + '_inp_' + input_name) sf = scaling_factors[i] self.declare_partials([output_name], [input_name], val=sf * sp.eye(vec_size * length, format='csc'))
[docs] def compute(self, inputs, outputs): """ Compute the element wise addition or subtraction of inputs using numpy + operator. Parameters ---------- inputs : Vector unscaled, dimensional input variables read via inputs[key] outputs : Vector unscaled, dimensional output variables read via outputs[key] """ complexify = self.options['complex'] for (output_name, input_names, vec_size, length, val, scaling_factors, kwargs) in self._add_systems: if isinstance(input_names, string_types): input_names = [input_names] if scaling_factors is None: scaling_factors = np.ones(len(input_names)) if length == 1: shape = (vec_size,) else: shape = (vec_size, length) if complexify: temp = np.zeros(shape, dtype=np.complex_) else: temp = np.zeros(shape) for i, input_name in enumerate(input_names): sf = scaling_factors[i] temp = temp + inputs[input_name] * sf outputs[output_name] = temp