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"""This module defines various OpenMDAO component classes.
"""
import numpy as np
import openmdao.api as omdao
[docs]class VecFunComponent(omdao.Component):
"""A component based on a list of functions.
A component that evaluates multiple functions on the given inputs, then
returns the result as an 1D array. Each of the inputs may be a scalar or
a vector with the same size as the output. If a vector input is given,
each function will use a different element of the vector.
Parameters
----------
output_name : str
output name.
fun_list : list[bag.math.dfun.DiffFunction]
list of interpolator functions, one for each dimension.
params : list[str]
list of parameter names. Parameter names may repeat, in which case the
same parameter will be used for multiple arguments of the function.
vector_params : set[str]
set of parameters that are vector instead of scalar. If a parameter
is a vector, it will be the same size as the output, and each function
only takes in the corresponding element of the parameter.
"""
def __init__(self, output_name, fun_list, params,
vector_params=None):
omdao.Component.__init__(self)
vector_params = vector_params or set()
self._output = output_name
self._out_dim = len(fun_list)
self._in_dim = len(params)
self._params = params
self._unique_params = {}
self._fun_list = fun_list
for par in params:
adj = par in vector_params
shape = self._out_dim if adj else 1
if par not in self._unique_params:
# linear check, but small list so should be fine.
self.add_param(par, val=np.zeros(shape))
self._unique_params[par] = len(self._unique_params), adj
# construct chain rule jacobian matrix
self._chain_jacobian = np.zeros((self._in_dim, len(self._unique_params)))
for idx, par in enumerate(params):
self._chain_jacobian[idx, self._unique_params[par][0]] = 1
self.add_output(output_name, val=np.zeros(self._out_dim))
[docs] def __call__(self, **kwargs):
"""Evaluate on the given inputs.
Parameters
----------
kwargs : dict[str, np.array or float]
the inputs as a dictionary.
Returns
-------
out : np.array
the output array.
"""
tmp = {}
self.solve_nonlinear(kwargs, tmp)
return tmp[self._output]
[docs] def solve_nonlinear(self, params, unknowns, resids=None):
"""Compute the output parameter.
Parameters
----------
params : VecWrapper, optional
VecWrapper containing parameters. (p)
unknowns : VecWrapper, optional
VecWrapper containing outputs and states. (u)
resids : VecWrapper, optional
VecWrapper containing residuals. (r)
"""
xi_mat = self._get_inputs(params)
tmp = np.empty(self._out_dim)
for idx in range(self._out_dim):
tmp[idx] = self._fun_list[idx](xi_mat[idx, :])
unknowns[self._output] = tmp
[docs] def linearize(self, params, unknowns=None, resids=None):
"""Compute the Jacobian of the parameter.
Parameters
----------
params : VecWrapper, optional
VecWrapper containing parameters. (p)
unknowns : VecWrapper, optional
VecWrapper containing outputs and states. (u)
resids : VecWrapper, optional
VecWrapper containing residuals. (r)
"""
# print('rank {} computing jac for {}'.format(self.comm.rank, self._outputs))
xi_mat = self._get_inputs(params)
jf = np.empty((self._out_dim, self._in_dim))
for k, fun in enumerate(self._fun_list):
jf[k, :] = fun.jacobian(xi_mat[k, :])
jmat = np.dot(jf, self._chain_jacobian)
jdict = {}
for par, (pidx, adj) in self._unique_params.items():
tmp = jmat[:, pidx]
if adj:
tmp = np.diag(tmp)
jdict[self._output, par] = tmp
return jdict