"""Define the default Vector class."""
from collections import defaultdict
import hashlib
import numpy as np
from openmdao.vectors.vector import Vector, _full_slice
from openmdao.vectors.default_transfer import DefaultTransfer
from openmdao.utils.array_utils import array_hash
[docs]
class DefaultVector(Vector):
"""
Default NumPy vector.
Parameters
----------
name : str
The name of the vector: 'nonlinear' or 'linear'.
kind : str
The kind of vector, 'input', 'output', or 'residual'.
system : <System>
Pointer to the owning system.
root_vector : <Vector>
Pointer to the vector owned by the root system.
alloc_complex : bool
Whether to allocate any imaginary storage to perform complex step. Default is False.
Attributes
----------
_views_rel : dict or None
If owning system is a component, this will contain a mapping of relative names to views.
"""
TRANSFER = DefaultTransfer
[docs]
def __init__(self, name, kind, system, root_vector=None, alloc_complex=False):
"""
Initialize all attributes.
"""
self._views_rel = None
super().__init__(name, kind, system, root_vector=root_vector, alloc_complex=alloc_complex)
[docs]
def __getitem__(self, name):
"""
Get the variable value.
Parameters
----------
name : str
Promoted or relative variable name in the owning system's namespace.
Returns
-------
float or ndarray
variable value.
"""
if self._views_rel is not None:
try:
if self._under_complex_step:
return self._views_rel[name]
return self._views_rel[name].real
except KeyError:
pass # try normal lookup after rel lookup failed
return super().__getitem__(name)
[docs]
def __setitem__(self, name, value):
"""
Set the variable value.
Parameters
----------
name : str
Promoted or relative variable name in the owning system's namespace.
value : float or list or tuple or ndarray
variable value to set
"""
if self._views_rel is not None and not self.read_only:
try:
self._views_rel[name][:] = value
return
except Exception:
pass # fall through to normal set if fast one failed in any way
self.set_var(name, value)
def _get_data(self):
"""
Return either the data array or its real part.
Note that this is intended to return only the _data array and not, for example,
to return a combined array in the case of an input vector that shares entries with
a connected output vector (for no-copy transfers).
Returns
-------
ndarray
The data array or its real part.
"""
return self._data if self._under_complex_step else self._data.real
def _create_data(self):
"""
Allocate data array.
This happens only in the top level system. Child systems use views of the array
we allocate here.
Returns
-------
ndarray
zeros array of correct size to hold all of this vector's variables.
"""
system = self._system()
size = np.sum(system._var_sizes[self._typ][system.comm.rank, :])
dtype = complex if self._alloc_complex else float
return np.zeros(size, dtype=dtype)
def _extract_root_data(self):
"""
Extract views of arrays from root_vector.
Returns
-------
ndarray
zeros array of correct size.
"""
system = self._system()
type_ = self._typ
root_vec = self._root_vector
slices = root_vec.get_slice_dict()
mynames = list(system._var_abs2meta[type_])
if mynames:
myslice = slice(slices[mynames[0]].start, slices[mynames[-1]].stop)
else:
myslice = slice(0, 0)
data = root_vec._data[myslice]
self._root_offset = myslice.start
scaling = None
if self._do_scaling:
root_scale = root_vec._scaling
rs0 = root_scale[0]
if rs0 is None:
scaling = (rs0, root_scale[1][myslice])
else:
scaling = (rs0[myslice], root_scale[1][myslice])
return data, scaling
def _initialize_data(self, root_vector):
"""
Internally allocate data array.
Parameters
----------
root_vector : Vector or None
the root's vector instance or None, if we are at the root.
"""
if root_vector is None: # we're the root
self._data = self._create_data()
if self._do_scaling:
data = self._data
if self._name == 'nonlinear':
if self._do_adder:
self._scaling = (np.zeros(data.size), np.ones(data.size))
else:
self._scaling = (None, np.ones(data.size))
elif self._name == 'linear':
if self._has_solver_ref:
# We only allocate an extra scaling vector when we have output scaling
# somewhere in the model.
self._scaling = (None, np.ones(data.size))
else:
# Reuse the nonlinear scaling vecs since they're the same as ours.
nlvec = self._system()._root_vecs[self._kind]['nonlinear']
self._scaling = (None, nlvec._scaling[1])
else:
self._scaling = (None, np.ones(data.size))
else:
self._data, self._scaling = self._extract_root_data()
def _initialize_views(self):
"""
Internally assemble views onto the vectors.
"""
system = self._system()
io = self._typ
kind = self._kind
islinear = self._name == 'linear'
rel_lookup = system._has_fast_rel_lookup()
do_scaling = self._do_scaling
if do_scaling:
factors = system._scale_factors
scaling = self._scaling
self._views = views = {}
self._views_flat = views_flat = {}
if rel_lookup:
self._views_rel = views_rel = {}
relstart = len(system.pathname) + 1 if system.pathname else 0
else:
self._views_rel = None
start = end = 0
for abs_name, meta in system._var_abs2meta[io].items():
end = start + meta['size']
shape = meta['shape']
views_flat[abs_name] = v = self._data[start:end]
if shape != v.shape:
v = v.view()
v.shape = shape
views[abs_name] = v
if rel_lookup:
views_rel[abs_name[relstart:]] = v
if do_scaling:
factor_tuple = factors[abs_name][kind]
if len(factor_tuple) == 4:
# Only input vectors can have 4 factors. Linear input vectors need to be able
# to handle the unit and solver scaling in opposite directions in reverse mode.
a0, a1, factor, offset = factor_tuple
if islinear:
scale0 = None
scale1 = factor / a1
else:
scale0 = (a0 + offset) * factor
scale1 = a1 * factor
else:
if self._name == 'linear' and self._typ == 'input':
scale0 = None
scale1 = 1.0 / factor_tuple[1]
else:
scale0, scale1 = factor_tuple
if scaling[0] is not None:
scaling[0][start:end] = scale0
scaling[1][start:end] = scale1
start = end
self._names = frozenset(views) if islinear else views
self._len = end
def _in_matvec_context(self):
"""
Return True if this vector is inside of a matvec_context.
Returns
-------
bool
Whether or not this vector is in a matvec_context.
"""
return len(self._names) != len(self._views)
def __iadd__(self, vec):
"""
Perform in-place vector addition.
Parameters
----------
vec : <Vector>
vector to add to self.
Returns
-------
<Vector>
self + vec
"""
if isinstance(vec, Vector):
self.iadd(vec.asarray())
else:
data = self.asarray()
data += vec
return self
def __isub__(self, vec):
"""
Perform in-place vector substraction.
Parameters
----------
vec : <Vector>
vector to subtract from self.
Returns
-------
<Vector>
self - vec
"""
if isinstance(vec, Vector):
self.isub(vec.asarray())
else:
data = self.asarray()
data -= vec
return self
def __imul__(self, vec):
"""
Perform in-place multiplication.
Parameters
----------
vec : Vector, int, float or ndarray
Value to multiply self.
Returns
-------
<Vector>
self * vec
"""
if isinstance(vec, Vector):
self.imul(vec.asarray())
else:
data = self.asarray()
data *= vec
return self
[docs]
def add_scal_vec(self, val, vec):
"""
Perform in-place addition of a vector times a scalar.
Parameters
----------
val : int or float
Scalar.
vec : <Vector>
This vector times val is added to self.
"""
data = self.asarray()
data += (val * vec.asarray())
[docs]
def set_vec(self, vec):
"""
Set the value of this vector to that of the incoming vector.
Parameters
----------
vec : <Vector>
The vector whose values self is set to.
"""
self.set_val(vec.asarray())
[docs]
def set_val(self, val, idxs=_full_slice):
"""
Set the data array of this vector to a value, with optional indexing.
Parameters
----------
val : float or ndarray
Scalar or array to set data array to.
idxs : int or slice or tuple of ints and/or slices
The locations where the data array should be updated.
"""
# we use _data here specifically so that imaginary part
# will get properly reset, e.g. when the array is zeroed out.
self._data[idxs] = val
[docs]
def scale_to_norm(self, mode='fwd'):
"""
Scale this vector to normalized form.
Parameters
----------
mode : str
Derivative direction.
"""
if self._has_solver_ref and mode == 'fwd':
scaler = self._scaling_nl_vec[1]
adder = None
else:
adder, scaler = self._scaling
if mode == 'rev':
self._scale_reverse(scaler, adder)
else:
self._scale_forward(scaler, adder)
[docs]
def scale_to_phys(self, mode='fwd'):
"""
Scale this vector to physical form.
Parameters
----------
mode : str
Derivative direction.
"""
if self._has_solver_ref and mode == 'fwd':
scaler = self._scaling_nl_vec[1]
adder = None
else:
adder, scaler = self._scaling
if mode == 'rev':
self._scale_forward(scaler, adder)
else:
self._scale_reverse(scaler, adder)
def _scale_forward(self, scaler, adder):
"""
Scale this vector by subtracting the adder and dividing by the scaler.
Parameters
----------
scaler : darray
Vector of multiplicative scaling factors.
adder : darray
Vector of additive scaling factors.
"""
data = self.asarray()
if adder is not None: # nonlinear only
data -= adder
data /= scaler
def _scale_reverse(self, scaler, adder):
"""
Scale this vector by multiplying by the scaler ahd adding the adder.
Parameters
----------
scaler : darray
Vector of multiplicative scaling factors.
adder : darray
Vector of additive scaling factors.
"""
data = self.asarray()
data *= scaler
if adder is not None: # nonlinear only
data += adder
[docs]
def asarray(self, copy=False):
"""
Return an array representation of this vector.
If copy is True, return a copy.
Parameters
----------
copy : bool
If True, return a copy of the array.
Returns
-------
ndarray
Array representation of this vector.
"""
if self._under_complex_step:
arr = self._data
else:
arr = self._data.real
if copy:
return arr.copy()
return arr
[docs]
def iscomplex(self):
"""
Return True if this vector contains complex values.
This checks the type of the values, not whether they have a nonzero imaginary part.
Returns
-------
bool
True if this vector contains complex values.
"""
return np.iscomplexobj(self._get_data())
[docs]
def iadd(self, val, idxs=_full_slice):
"""
Add the value to the data array at the specified indices or slice(s).
Parameters
----------
val : ndarray
Value to set into the data array.
idxs : int or slice or tuple of ints and/or slices
The locations where the data array should be updated.
"""
data = self.asarray()
data[idxs] += val
[docs]
def isub(self, val, idxs=_full_slice):
"""
Subtract the value from the data array at the specified indices or slice(s).
Parameters
----------
val : ndarray
Value to set into the data array.
idxs : int or slice or tuple of ints and/or slices
The locations where the data array should be updated.
"""
data = self.asarray()
data[idxs] -= val
[docs]
def imul(self, val, idxs=_full_slice):
"""
Multiply the value to the data array at the specified indices or slice(s).
Parameters
----------
val : ndarray
Value to set into the data array.
idxs : int or slice or tuple of ints and/or slices
The locations where the data array should be updated.
"""
data = self.asarray()
data[idxs] *= val
[docs]
def dot(self, vec):
"""
Compute the dot product of the real parts of the current vec and the incoming vec.
Parameters
----------
vec : <Vector>
The incoming vector being dotted with self.
Returns
-------
float
The computed dot product value.
"""
return np.dot(self.asarray(), vec.asarray())
[docs]
def get_norm(self):
"""
Return the norm of this vector.
Returns
-------
float
Norm of this vector.
"""
return np.linalg.norm(self.asarray())
[docs]
def get_slice_dict(self):
"""
Return a dict of var names mapped to their slice in the local data array.
Returns
-------
dict
Mapping of var name to slice.
"""
if self._slices is None:
slices = {}
start = end = 0
for name, arr in self._views_flat.items():
end += arr.size
slices[name] = slice(start, end)
start = end
self._slices = slices
return self._slices
[docs]
def idxs2nameloc(self, idxs):
"""
Given some indices, return a dict mapping variable name to corresponding local indices.
This is slow and is meant to be used only for debugging or maybe error messages.
Parameters
----------
idxs : list of int
Vector indices to be converted to local indices for each corresponding variable.
Returns
-------
dict
Mapping of variable name to a list of local indices into that variable.
"""
name2inds = defaultdict(list)
start = end = 0
for name, arr in self._views_flat.items():
end += arr.size
for idx in idxs:
if start <= idx < end:
name2inds[name].append(idx - start)
start = end
return name2inds
def __getstate__(self):
"""
Return state as a dict.
For pickling vectors in case recording, we want to get rid of
the system contained within Vectors, because MPI Comm objects cannot
be pickled using Python3's pickle module.
Returns
-------
dict
state minus system member.
"""
state = self.__dict__.copy()
if '_system' in state:
del state['_system']
return state
[docs]
def get_hash(self, alg=hashlib.sha1):
"""
Return a hash string for the array contained in this Vector.
Parameters
----------
alg : function
Algorithm used to generate the hash. Default is hashlib.sha1.
Returns
-------
str
The hash string.
"""
if self._data.size == 0:
return ''
# we must use self._data here because the hashing alg requires array to be C-contiguous
return array_hash(self._data, alg)
