"""Define the base Vector and Transfer classes."""
from copy import deepcopy
import weakref
import hashlib
import numpy as np
from numpy import ndim
from openmdao.utils.name_maps import prom_name2abs_name
from openmdao.utils.indexer import Indexer, indexer
_full_slice = slice(None)
_flat_full_indexer = indexer(_full_slice, flat_src=True)
_full_indexer = indexer(_full_slice, flat_src=False)
_type_map = {
'input': 'input',
'output': 'output',
'residual': 'output'
}
[docs]class Vector(object):
"""
Base Vector class.
This class is instantiated for inputs, outputs, and residuals.
It provides a dictionary interface and an arithmetic operations interface.
Implementations:
- <DefaultVector>
- <PETScVector>
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
----------
_name : str
The name of the vector: 'nonlinear' or 'linear'.
_typ : str
Type: 'input' for input vectors; 'output' for output/residual vectors.
_kind : str
Specific kind of vector, either 'input', 'output', or 'residual'.
_system : System
Pointer to the owning system.
_iproc : int
Global processor index.
_length : int
Length of flattened vector.
_views : dict
Dictionary mapping absolute variable names to the ndarray views.
_views_flat : dict
Dictionary mapping absolute variable names to the flattened ndarray views.
_names : set([str, ...])
Set of variables that are relevant in the current context.
_root_vector : Vector
Pointer to the vector owned by the root system.
_root_offset : int
Offset of this vector into the root vector.
_alloc_complex : bool
If True, then space for the complex vector is also allocated.
_data : ndarray
Actual allocated data.
_slices : dict
Mapping of var name to slice.
_under_complex_step : bool
When True, this vector is under complex step, and data is swapped with the complex data.
_do_scaling : bool
True if this vector performs scaling.
_do_adder : bool
True if this vector's scaling includes an additive term.
_scaling : dict
Contains scale factors to convert data arrays.
_scaling_nl_vec : dict
Reference to the scaling factors in the nonlinear vector. Only used for linear input
vectors.
read_only : bool
When True, values in the vector cannot be changed via the user __setitem__ API.
_len : int
Total length of data vector (including shared memory parts).
_has_solver_ref : bool
This is set to True only when a ref is defined on a solver.
"""
# Listing of relevant citations
cite = ""
# Indicator whether a vector class is MPI-distributed
distributed = False
[docs] def __init__(self, name, kind, system, root_vector=None, alloc_complex=False):
"""
Initialize all attributes.
"""
self._name = name
self._typ = _type_map[kind]
self._kind = kind
self._len = 0
self._system = weakref.ref(system)
self._views = {}
self._views_flat = {}
# self._names will either contain the same names as self._views or to the
# set of variables relevant to the current matvec product.
self._names = self._views
self._root_vector = None
self._data = None
self._slices = None
self._root_offset = 0
# Support for Complex Step
self._alloc_complex = alloc_complex
self._under_complex_step = False
self._do_scaling = ((kind == 'input' and system._has_input_scaling) or
(kind == 'output' and system._has_output_scaling) or
(kind == 'residual' and system._has_resid_scaling))
self._do_adder = ((kind == 'input' and system._has_input_adder) or
(kind == 'output' and system._has_output_adder) or
(kind == 'residual' and system._has_resid_scaling))
self._scaling = None
self._scaling_nl_vec = None
# If we define 'ref' on an output, then we will need to allocate a separate scaling ndarray
# for the linear and nonlinear input vectors.
self._has_solver_ref = system._has_output_scaling and kind == 'input' and name == 'linear'
if root_vector is None:
self._root_vector = self
else:
self._root_vector = root_vector
self._initialize_data(root_vector)
self._initialize_views()
self.read_only = False
def __str__(self):
"""
Return a string representation of the Vector object.
Returns
-------
str
String rep of this object.
"""
return str(self.asarray())
def __len__(self):
"""
Return the flattened length of this Vector.
Returns
-------
int
Total flattened length of this vector.
"""
return self._len
[docs] def nvars(self):
"""
Return the number of variables in this Vector.
Returns
-------
int
Number of variables in this Vector.
"""
return len(self._views)
def _copy_views(self):
"""
Return a dictionary containing just the views.
Returns
-------
dict
Dictionary containing the _views.
"""
return deepcopy(self._views)
[docs] def keys(self):
"""
Return variable names of variables contained in this vector (relative names).
Returns
-------
listiterator (Python 3.x) or list (Python 2.x)
The variable names.
"""
return self.__iter__()
[docs] def values(self):
"""
Return values of variables contained in this vector.
Yields
------
ndarray or float
Value of each variable.
"""
if self._under_complex_step:
for n, v in self._views.items():
if n in self._names:
yield v
else:
for n, v in self._views.items():
if n in self._names:
yield v.real
[docs] def items(self):
"""
Return (name, value) for variables contained in this vector.
Yields
------
str
Name of each variable.
ndarray or float
Value of each variable.
"""
if self._under_complex_step:
for n, v in self._views.items():
if n in self._names:
yield n, v
else:
for n, v in self._views.items():
if n in self._names:
yield n, v.real
def _name2abs_name(self, name):
"""
Map the given promoted or relative name to the absolute name.
This is only valid when the name is unique; otherwise, a KeyError is thrown.
Parameters
----------
name : str
Promoted or relative variable name in the owning system's namespace.
Returns
-------
str or None
Absolute variable name if unique abs_name found or None otherwise.
"""
system = self._system()
# try relative name first
abs_name = system.pathname + '.' + name if system.pathname else name
if abs_name in self._views:
return abs_name
abs_name = prom_name2abs_name(system, name, self._typ)
if abs_name in self._views:
return abs_name
[docs] def __iter__(self):
"""
Return an iterator over variables involved in the current mat-vec product (relative names).
Returns
-------
listiterator
iterator over the variable names.
"""
system = self._system()
path = system.pathname
idx = len(path) + 1 if path else 0
return (n[idx:] for n in system._var_abs2meta[self._typ] if n in self._names)
def _abs_item_iter(self, flat=True):
"""
Iterate over the items in the vector, using absolute names.
Parameters
----------
flat : bool
If True, return the flattened values.
Yields
------
str
Name of each variable.
ndarray or float
Value of each variable.
"""
arrs = self._views_flat if flat else self._views
if self._under_complex_step:
yield from arrs.items()
else:
for name, val in arrs.items():
yield name, val.real
def _abs_iter(self):
"""
Iterate over the absolute names in the vector.
Yields
------
str
Name of each variable.
"""
yield from self._views
[docs] def __contains__(self, name):
"""
Check if the variable is found in this vector.
Parameters
----------
name : str
Promoted or relative variable name in the owning system's namespace.
Returns
-------
bool
True or False.
"""
return self._name2abs_name(name) in self._names
def _contains_abs(self, name):
"""
Check if the variable is found in this vector.
Parameters
----------
name : str
Absolute variable name.
Returns
-------
bool
True or False.
"""
return name in self._names
[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.
"""
abs_name = self._name2abs_name(name)
if abs_name is not None:
return self._abs_get_val(abs_name, flat=False)
else:
raise KeyError(f"{self._system().msginfo}: Variable name '{name}' not found.")
def _abs_get_val(self, name, flat=True):
"""
Get the variable value using the absolute name.
No error checking is performed on the name.
Parameters
----------
name : str
Absolute name in the owning system's namespace.
flat : bool
If True, return the flat value.
Returns
-------
float or ndarray
variable value.
"""
if flat:
val = self._views_flat[name]
else:
val = self._views[name]
if self._under_complex_step:
return val
return val.real
[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
"""
self.set_var(name, value)
def _initialize_data(self, root_vector):
"""
Internally allocate vectors.
Must be implemented by the subclass.
Parameters
----------
root_vector : <Vector> or None
the root's vector instance or None, if we are at the root.
"""
raise NotImplementedError('_initialize_data not defined for vector type '
f'{type(self).__name__}')
def _initialize_views(self):
"""
Internally assemble views onto the vectors.
Must be implemented by the subclass.
"""
raise NotImplementedError('_initialize_views not defined for vector type '
f'{type(self).__name__}')
def __iadd__(self, vec):
"""
Perform in-place vector addition.
Must be implemented by the subclass.
Parameters
----------
vec : <Vector>
vector to add to self.
"""
raise NotImplementedError(f'__iadd__ not defined for vector type {type(self).__name__}')
def __isub__(self, vec):
"""
Perform in-place vector substraction.
Must be implemented by the subclass.
Parameters
----------
vec : <Vector>
vector to subtract from self.
"""
raise NotImplementedError(f'__isub__ not defined for vector type {type(self).__name__}')
def __imul__(self, val):
"""
Perform in-place scalar multiplication.
Must be implemented by the subclass.
Parameters
----------
val : int or float
scalar to multiply self.
"""
raise NotImplementedError(f'__imul__ not defined for vector type {type(self).__name__}')
[docs] def add_scal_vec(self, val, vec):
"""
Perform in-place addition of a vector times a scalar.
Must be implemented by the subclass.
Parameters
----------
val : int or float
Scalar.
vec : <Vector>
This vector times val is added to self.
"""
raise NotImplementedError('add_scale_vec not defined for vector type '
f'{ype(self).__name__}')
[docs] def asarray(self, copy=False):
"""
Return a flat array representation of this vector.
If copy is True, return a copy. Otherwise, try to avoid it.
Parameters
----------
copy : bool
If True, return a copy of the array.
Returns
-------
ndarray
Array representation of this vector.
"""
raise NotImplementedError(f'asarray not defined for vector type {type(self).__name__}')
return None # silence lint warning
[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.
"""
raise NotImplementedError(f'iscomplex not defined for vector type {type(self).__name__}')
return False # silence lint warning
[docs] def set_vec(self, vec):
"""
Set the value of this vector to that of the incoming vector.
Must be implemented by the subclass.
Parameters
----------
vec : <Vector>
The vector whose values self is set to.
"""
raise NotImplementedError(f'set_vec not defined for vector type {type(self).__name__}')
[docs] def set_val(self, val, idxs=_full_slice):
"""
Set the data array of this vector to a scalar or array value, with optional indices.
Must be implemented by the subclass.
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.
"""
raise NotImplementedError(f'set_arr not defined for vector type {type(self).__name__}')
[docs] def set_vals(self, vals):
"""
Set the data array of this vector using a value or iter of values, one for each variable.
The values must be in the same order as the variables appear in this Vector.
Parameters
----------
vals : ndarray, float, or iter of ndarrays and/or floats
Values for each variable contained in this vector, in the proper order.
"""
arr = self.asarray()
if self.nvars() == 1:
if ndim(vals) == 0:
arr[:] = vals
else:
arr[:] = vals.ravel()
else:
start = end = 0
for v in vals:
if ndim(v) == 0:
end += 1
arr[start] = v
else:
end += v.size
arr[start:end] = v.ravel()
start = end
[docs] def set_var(self, name, val, idxs=_full_slice, flat=False, var_name=None):
"""
Set the array view corresponding to the named variable, with optional indexing.
Parameters
----------
name : str
The name of the variable.
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.
flat : bool
If True, set into flattened variable.
var_name : str or None
If specified, the variable name to use when reporting errors. This is useful
when setting an AutoIVC value that the user only knows by a connected input name.
"""
abs_name = self._name2abs_name(name)
if abs_name is None:
raise KeyError(f"{self._system().msginfo}: Variable name "
f"'{var_name if var_name else name}' not found.")
if self.read_only:
raise ValueError(f"{self._system().msginfo}: Attempt to set value of "
f"'{var_name if var_name else name}' in "
f"{self._kind} vector when it is read only.")
if idxs is _full_slice:
if flat:
idxs = _flat_full_indexer
else:
idxs = _full_indexer
elif not isinstance(idxs, Indexer):
idxs = indexer(idxs, flat_src=flat)
if flat:
if isinstance(val, float):
self._views_flat[abs_name][idxs.flat()] = val
else:
self._views_flat[abs_name][idxs.flat()] = np.asarray(val).flat
else:
value = np.asarray(val)
view = self._views[abs_name]
try:
if view.shape:
view[idxs()] = value
else:
# view is a scalar so we can't update it without breaking its connection
# to the underlying array, so set the value into the
# array using the flat view, which is an array of size 1.
self._views_flat[abs_name][0] = value
except Exception as err:
try:
value = value.reshape(view[idxs()].shape)
except Exception:
raise ValueError(f"{self._system().msginfo}: Failed to set value of "
f"'{var_name if var_name else name}': {str(err)}.")
view[idxs()] = value
[docs] def dot(self, vec):
"""
Compute the dot product of the current vec and the incoming vec.
Must be implemented by the subclass.
Parameters
----------
vec : <Vector>
The incoming vector being dotted with self.
"""
raise NotImplementedError(f'dot not defined for vector type {type(self).__name__}')
[docs] def get_norm(self):
"""
Return the norm of this vector.
Must be implemented by the subclass.
Returns
-------
float
Norm of this vector.
"""
raise NotImplementedError(f'get_norm not defined for vector type {type(self).__name__}')
return None # silence lint warning about missing return value.
def _in_matvec_context(self):
"""
Return True if this vector is inside of a matvec_context.
"""
raise NotImplementedError('_in_matvec_context not defined for vector type '
f'{type(self).__name__}')
[docs] def set_complex_step_mode(self, active):
"""
Turn on or off complex stepping mode.
Parameters
----------
active : bool
Complex mode flag; set to True prior to commencing complex step.
"""
self._under_complex_step = active
[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.
"""
raise NotImplementedError(f'get_hash not defined for vector type {type(self).__name__}')
return '' # silence lint warning about missing return value.
