Source code for openmdao.components.exec_comp

"""Define the ExecComp class, a component that evaluates an expression."""
import re
from itertools import product

import numpy as np
from numpy import ndarray, imag, complex as npcomplex

from six import string_types
from six.moves import range

from openmdao.core.explicitcomponent import ExplicitComponent
from openmdao.utils.units import valid_units
from openmdao.utils.general_utils import warn_deprecation

# regex to check for variable names.
VAR_RGX = re.compile(r'([.]*[_a-zA-Z]\w*[ ]*\(?)')

# Names of metadata entries allowed for ExecComp variables.
_allowed_meta = {'value', 'shape', 'units', 'res_units', 'desc',
                 'ref', 'ref0', 'res_ref', 'lower', 'upper', 'src_indices',
                 'flat_src_indices', 'tags'}

# Names that are not allowed for input or output variables (keywords for options)
_disallowed_names = {'has_diag_partials', 'vectorize', 'units', 'shape'}


[docs]def check_option(option, value): """ Check option for validity. Parameters ---------- option : str The name of the option value : any The value of the option Raises ------ ValueError """ if option is 'units' and value is not None and not valid_units(value): raise ValueError("The units '%s' are invalid." % value)
[docs]def array_idx_iter(shape): """ Return an iterator over the indices into a n-dimensional array. Parameters ---------- shape : tuple shape of the array. """ for p in product(*[range(s) for s in shape]): yield p
[docs]class ExecComp(ExplicitComponent): """ A component defined by an expression string. Attributes ---------- _kwargs : dict of named args Initial values of variables. _exprs : list List of expressions. _codes : list List of code objects. _has_diag_partials : bool If True, treat all array/array partials as diagonal if both arrays have size > 1. All arrays with size > 1 must have the same flattened size or an exception will be raised. _units : str or None Units to be assigned to all variables in this component. Default is None, which means units are provided for variables individually. complex_stepsize : double Step size used for complex step which is used for derivatives. """
[docs] def initialize(self): """ Declare options. """ self.options.declare('has_diag_partials', types=bool, default=False, desc='If True, treat all array/array partials as diagonal if both ' 'arrays have size > 1. All arrays with size > 1 must have the ' 'same flattened size or an exception will be raised.') self.options.declare('units', types=str, allow_none=True, default=None, desc='Units to be assigned to all variables in this component. ' 'Default is None, which means units are provided for variables ' 'individually.', check_valid=check_option) self.options.declare('shape', types=(int, tuple, list), allow_none=True, default=None, desc='Shape to be assigned to all variables in this component. ' 'Default is None, which means shape is provided for variables ' 'individually.')
[docs] def __init__(self, exprs=[], **kwargs): r""" Create a <Component> using only an expression string. Given a list of assignment statements, this component creates input and output variables at construction time. All variables appearing on the left-hand side of an assignment are outputs, and the rest are inputs. Each variable is assumed to be of type float unless the initial value for that variable is supplied in \*\*kwargs. Derivatives are calculated using complex step. The following functions are available for use in expressions: ========================= ==================================== Function Description ========================= ==================================== abs(x) Absolute value of x acos(x) Inverse cosine of x acosh(x) Inverse hyperbolic cosine of x arange(start, stop, step) Array creation arccos(x) Inverse cosine of x arccosh(x) Inverse hyperbolic cosine of x arcsin(x) Inverse sine of x arcsinh(x) Inverse hyperbolic sine of x arctan(x) Inverse tangent of x asin(x) Inverse sine of x asinh(x) Inverse hyperbolic sine of x atan(x) Inverse tangent of x cos(x) Cosine of x cosh(x) Hyperbolic cosine of x dot(x, y) Dot product of x and y e Euler's number erf(x) Error function erfc(x) Complementary error function exp(x) Exponential function expm1(x) exp(x) - 1 factorial(x) Factorial of all numbers in x fmax(x, y) Element-wise maximum of x and y fmin(x, y) Element-wise minimum of x and y inner(x, y) Inner product of arrays x and y isinf(x) Element-wise detection of np.inf isnan(x) Element-wise detection of np.nan kron(x, y) Kronecker product of arrays x and y linspace(x, y, N) Numpy linear spaced array creation log(x) Natural logarithm of x log10(x) Base-10 logarithm of x log1p(x) log(1+x) matmul(x, y) Matrix multiplication of x and y maximum(x, y) Element-wise maximum of x and y minimum(x, y) Element-wise minimum of x and y ones(N) Create an array of ones outer(x, y) Outer product of x and y pi Pi power(x, y) Element-wise x**y prod(x) The product of all elements in x sin(x) Sine of x sinh(x) Hyperbolic sine of x sum(x) The sum of all elements in x tan(x) Tangent of x tanh(x) Hyperbolic tangent of x tensordot(x, y) Tensor dot product of x and y zeros(N) Create an array of zeros ========================= ==================================== Parameters ---------- exprs : str, tuple of str or list of str An assignment statement or iter of them. These express how the outputs are calculated based on the inputs. In addition to standard Python operators, a subset of numpy and scipy functions is supported. **kwargs : dict of named args Initial values of variables can be set by setting a named arg with the var name. If the value is a dict it is assumed to contain metadata. To set the initial value in addition to other metadata, assign the initial value to the 'value' entry of the dict. Notes ----- If a variable has an initial value that is anything other than 1.0, either because it has a different type than float or just because its initial value is != 1.0, you must use a keyword arg to set the initial value. For example, let's say we have an ExecComp that takes an array 'x' as input and outputs a float variable 'y' which is the sum of the entries in 'x'. .. code-block:: python import numpy import openmdao.api as om excomp = om.ExecComp('y=sum(x)', x=numpy.ones(10,dtype=float)) In this example, 'y' would be assumed to be the default type of float and would be given the default initial value of 1.0, while 'x' would be initialized with a size 10 float array of ones. If you want to assign certain metadata for 'x' in addition to its initial value, you can do it as follows: .. code-block:: python excomp = ExecComp('y=sum(x)', x={'value': numpy.ones(10,dtype=float), 'units': 'ft'}) """ # separate disallowed var names from kwargs, pass them as options to __init__ if 'vectorize' in kwargs: warn_deprecation("The 'vectorize' option is deprecated. " "Please use 'has_diag_partials' instead.") kwargs['has_diag_partials'] = kwargs['vectorize'] del kwargs['vectorize'] options = {} for name in _disallowed_names: if name in kwargs: options[name] = kwargs.pop(name) super(ExecComp, self).__init__(**options) # if complex step is used for derivatives, this is the stepsize self.complex_stepsize = 1.e-40 if isinstance(exprs, string_types): exprs = [exprs] self._exprs = exprs[:] self._codes = None self._kwargs = kwargs
[docs] def setup(self): """ Set up variable name and metadata lists. """ if not self._exprs: raise RuntimeError("%s: No valid expressions provided to ExecComp(): %s." % (self.msginfo, self._exprs)) outs = set() allvars = set() exprs = self._exprs kwargs = self._kwargs units = self.options['units'] shape = self.options['shape'] # find all of the variables and which ones are outputs for expr in exprs: lhs, _ = expr.split('=', 1) outs.update(self._parse_for_out_vars(lhs)) allvars.update(self._parse_for_vars(expr)) kwargs2 = {} init_vals = {} # make sure all kwargs are legit for arg, val in kwargs.items(): if arg not in allvars: raise RuntimeError("%s: arg '%s' in call to ExecComp() " "does not refer to any variable in the " "expressions %s" % (self.msginfo, arg, exprs)) if isinstance(val, dict): diff = set(val.keys()) - _allowed_meta if diff: raise RuntimeError("%s: the following metadata names were not " "recognized for variable '%s': %s" % (self.msginfo, arg, sorted(diff))) kwargs2[arg] = val.copy() if units is not None: if 'units' in val and val['units'] != units: raise RuntimeError("%s: units of '%s' have been specified for " "variable '%s', but units of '%s' have been " "specified for the entire component." % (self.msginfo, val['units'], arg, units)) else: kwargs2[arg]['units'] = units if shape is not None: if 'shape' in val and val['shape'] != shape: raise RuntimeError("%s: shape of %s has been specified for " "variable '%s', but shape of %s has been " "specified for the entire component." % (self.msginfo, val['shape'], arg, shape)) elif 'value' in val and np.atleast_1d(val['value']).shape != shape: raise RuntimeError("%s: value of shape %s has been specified for " "variable '%s', but shape of %s has been " "specified for the entire component." % (self.msginfo, np.atleast_1d(val['value']).shape, arg, shape)) else: init_vals[arg] = np.ones(shape) if 'value' in val: init_vals[arg] = val['value'] del kwargs2[arg]['value'] if 'shape' in val: if arg not in init_vals: init_vals[arg] = np.ones(val['shape']) elif np.atleast_1d(init_vals[arg]).shape != val['shape']: raise RuntimeError("%s: shape of %s has been specified for variable " "'%s', but a value of shape %s has been provided." % (self.msginfo, str(val['shape']), arg, str(np.atleast_1d(init_vals[arg]).shape))) del kwargs2[arg]['shape'] else: init_vals[arg] = val for var in sorted(allvars): # if user supplied an initial value, use it, otherwise set to 1.0 if var in init_vals: val = init_vals[var] else: init_vals[var] = val = 1.0 meta = kwargs2.get(var, {'units': units, 'shape': shape}) if var in outs: self.add_output(var, val, **meta) else: self.add_input(var, val, **meta) if self.options['has_diag_partials']: # check that sizes of any input/output vars match or one of them is size 1 osorted = sorted(self._var_rel_names['output']) for inp in sorted(self._var_rel_names['input']): ival = init_vals[inp] iarray = isinstance(ival, ndarray) and ival.size > 1 for out in osorted: oval = init_vals[out] if (iarray and isinstance(oval, ndarray) and oval.size > 1): if oval.size != ival.size: raise RuntimeError("%s: has_diag_partials is True but partial(%s, %s) " "is not square (shape=(%d, %d))." % (self.msginfo, out, inp, oval.size, ival.size)) # partial will be declared as diagonal inds = np.arange(oval.size, dtype=int) else: inds = None self.declare_partials(of=out, wrt=inp, rows=inds, cols=inds) else: # All derivatives are defined as dense self.declare_partials(of='*', wrt='*') self._codes = self._compile_exprs(self._exprs)
def _compile_exprs(self, exprs): compiled = [] for i, expr in enumerate(exprs): try: compiled.append(compile(expr, expr, 'exec')) except Exception: raise RuntimeError("%s: failed to compile expression '%s'." % (self.msginfo, exprs[i])) return compiled def _parse_for_out_vars(self, s): vnames = set([x.strip() for x in re.findall(VAR_RGX, s) if not x.endswith('(') and not x.startswith('.')]) for v in vnames: if v in _expr_dict: raise NameError("%s: cannot assign to variable '%s' " "because it's already defined as an internal " "function or constant." % (self.msginfo, v)) return vnames def _parse_for_vars(self, s): vnames = set([x.strip() for x in re.findall(VAR_RGX, s) if not x.endswith('(') and not x.startswith('.')]) to_remove = [] for v in vnames: if v in _disallowed_names: raise NameError("%s: cannot use variable name '%s' because " "it's a reserved keyword." % (self.msginfo, v)) if v in _expr_dict: expvar = _expr_dict[v] if callable(expvar): raise NameError("%s: cannot use '%s' as a variable because " "it's already defined as an internal " "function." % (self.msginfo, v)) else: to_remove.append(v) return vnames.difference(to_remove) def __getstate__(self): """ Return state as a dict. Returns ------- dict State to get. """ state = self.__dict__.copy() del state['_codes'] return state def __setstate__(self, state): """ Restore state from `state`. Parameters ---------- state : dict State to restore. """ self.__dict__.update(state) self._codes = self._compile_exprs(self._exprs)
[docs] def compute(self, inputs, outputs): """ Execute this component's assignment statements. Parameters ---------- inputs : `Vector` `Vector` containing inputs. outputs : `Vector` `Vector` containing outputs. """ for i, expr in enumerate(self._codes): try: exec(expr, _expr_dict, _IODict(outputs, inputs)) except Exception as err: raise RuntimeError("%s: Error occurred evaluating '%s'\n%s" % (self.msginfo, self._exprs[i], str(err)))
[docs] def compute_partials(self, inputs, partials): """ Use complex step method to update the given Jacobian. Parameters ---------- inputs : `VecWrapper` `VecWrapper` containing parameters. (p) partials : `Jacobian` Contains sub-jacobians. """ step = self.complex_stepsize * 1j out_names = self._var_allprocs_prom2abs_list['output'] inv_stepsize = 1.0 / self.complex_stepsize has_diag_partials = self.options['has_diag_partials'] for param in inputs: pwrap = _TmpDict(inputs) pval = inputs[param] psize = pval.size pwrap[param] = np.asarray(pval, npcomplex) if has_diag_partials or psize == 1: # set a complex param value pwrap[param] += step uwrap = _TmpDict(self._outputs, return_complex=True) # solve with complex param value self._residuals.set_const(0.0) self.compute(pwrap, uwrap) for u in out_names: partials[(u, param)] = imag(uwrap[u] * inv_stepsize).flat # restore old param value pwrap[param] -= step else: for i, idx in enumerate(array_idx_iter(pwrap[param].shape)): # set a complex param value pwrap[param][idx] += step uwrap = _TmpDict(self._outputs, return_complex=True) # solve with complex param value self._residuals.set_const(0.0) self.compute(pwrap, uwrap) for u in out_names: # set the column in the Jacobian entry partials[(u, param)][:, i] = imag(uwrap[u] * inv_stepsize).flat # restore old param value pwrap[param][idx] -= step
class _TmpDict(object): """ Dict wrapper that allows modification without changing the wrapped dict. It will allow getting of values from its inner dict unless those values get modified via __setitem__. After values have been modified they are managed thereafter by the wrapper. This protects the inner dict from modification. Attributes ---------- _inner : dict-like The dictionary to be wrapped. _changed : dict-like The key names for the values that were changed. _complex : bool If True, return a complex version of values from __getitem__. """ def __init__(self, inner, return_complex=False): """ Construct the dictionary object. Parameters ---------- inner : dict-like The dictionary to be wrapped. return_complex : bool, optional If True, return a complex version of values from __getitem__ """ self._inner = inner self._changed = {} self._complex = return_complex def __getitem__(self, name): if name in self._changed: return self._changed[name] elif self._complex: val = self._inner[name] if isinstance(val, ndarray): self._changed[name] = np.asarray(val, dtype=npcomplex) else: self._changed[name] = npcomplex(val) return self._changed[name] else: return self._inner[name] def __setitem__(self, name, value): self._changed[name] = value def __contains__(self, name): return name in self._inner or name in self._changed def __getattr__(self, name): return getattr(self._inner, name) class _IODict(object): """ A dict wrapper that contains 2 different dicts. Items are first looked for in the outputs and then the inputs. Attributes ---------- _inputs : dict-like The inputs object to be wrapped. _outputs : dict-like The outputs object to be wrapped. """ def __init__(self, outputs, inputs): """ Create the dict wrapper. Parameters ---------- outputs : dict-like The outputs object to be wrapped. inputs : dict-like The inputs object to be wrapped. """ self._outputs = outputs self._inputs = inputs def __getitem__(self, name): if name in self._outputs: return self._outputs[name] else: return self._inputs[name] def __setitem__(self, name, value): if name in self._outputs: self._outputs[name] = value elif name in self._inputs: self._inputs[name] = value else: self._outputs[name] = value # will raise KeyError def __contains__(self, name): return name in self._outputs or name in self._inputs def _import_functs(mod, dct, names=None): """ Map attributes attrs from the given module into the given dict. Parameters ---------- mod : object Module to check. dct : dict Dictionary that will contain the mapping names : iter of str, optional If supplied, only map attrs that match the given names """ if names is None: names = dir(mod) for name in names: if isinstance(name, tuple): name, alias = name else: alias = name if not name[0] == '_': dct[name] = getattr(mod, name) dct[alias] = dct[name] # this dict will act as the local scope when we eval our expressions _expr_dict = {} _import_functs(np, _expr_dict, names=['arange', 'ones', 'zeros', 'linspace', # Array creation 'e', 'pi', # Constants 'isinf', 'isnan', # Logic 'log', 'log10', 'log1p', 'power', # Math operations 'exp', 'expm1', 'fmax', 'fmin', 'maximum', 'minimum', 'sum', 'dot', 'prod', # Reductions 'tensordot', 'matmul', # Linear algebra 'outer', 'inner', 'kron', 'sin', 'cos', 'tan', ('arcsin', 'asin'), # Trig ('arccos', 'acos'), ('arctan', 'atan'), 'sinh', 'cosh', 'tanh', ('arcsinh', 'asinh'), # Hyperbolic trig ('arccosh', 'acosh')]) # if scipy is available, add some functions try: import scipy.special except ImportError: pass else: _import_functs(scipy.special, _expr_dict, names=['factorial', 'erf', 'erfc']) # Put any functions here that need special versions to work under # complex step def _cs_abs(x): if isinstance(x, ndarray): return x * np.sign(x) elif x.real < 0.0: return -x return x _expr_dict['abs'] = _cs_abs class _NumpyMsg(object): """ A class that will raise an error if an attempt is made to access any attribute/function. """ def __init__(self, namespace): """ Construct the _NumpyMsg object. Parameters ---------- namespace : str The numpy namespace (e.g. 'numpy' or 'np). """ self.namespace = namespace def __getattr__(self, name): """ Attempt to access an attribute/function. Parameters ---------- name : str The name of the attribute/function. Raises ------ RuntimeError When an attempt is made to access any attribute/function. """ raise RuntimeError('\n'.join([ " ExecComp supports a subset of numpy functions directly, without the '%s' prefix.", " '%s' is %ssupported (See the documentation)." ]) % (self.namespace, name, '' if name in _expr_dict else 'not ')) _expr_dict['np'] = _NumpyMsg('np') _expr_dict['numpy'] = _NumpyMsg('numpy')