"""
C++ Export
----------
This module provides all necessary functionality specify an DE model and
generate executable C++ simulation code. The user generally won't have to
directly call any function from this module as this will be done by
:py:func:`amici.pysb_import.pysb2amici`,
:py:func:`amici.sbml_import.SbmlImporter.sbml2amici` and
:py:func:`amici.petab_import.import_model`.
"""
import contextlib
import copy
import itertools
import logging
import os
import re
import shutil
import subprocess
import sys
from dataclasses import dataclass
from itertools import chain, starmap
from pathlib import Path
from string import Template
from typing import (Any, Callable, Dict, List, Optional, Sequence, Set, Tuple,
Union)
import numpy as np
import sympy as sp
from sympy.matrices.dense import MutableDenseMatrix
from sympy.matrices.immutable import ImmutableDenseMatrix
from . import (__commit__, __version__, amiciModulePath, amiciSrcPath,
amiciSwigPath, sbml_import)
from .constants import SymbolId
from .cxxcodeprinter import AmiciCxxCodePrinter, get_switch_statement
from .import_utils import (ObservableTransformation, generate_flux_symbol,
smart_subs_dict, strip_pysb,
symbol_with_assumptions, toposort_symbols,
SBMLException)
from .logging import get_logger, log_execution_time, set_log_level
from .de_model import *
# Template for model simulation main.cpp file
CXX_MAIN_TEMPLATE_FILE = os.path.join(amiciSrcPath, 'main.template.cpp')
# Template for model/swig/CMakeLists.txt
SWIG_CMAKE_TEMPLATE_FILE = os.path.join(amiciSwigPath,
'CMakeLists_model.cmake')
# Template for model/CMakeLists.txt
MODEL_CMAKE_TEMPLATE_FILE = os.path.join(amiciSrcPath,
'CMakeLists.template.cmake')
IDENTIFIER_PATTERN = re.compile(r'^[a-zA-Z_]\w*$')
DERIVATIVE_PATTERN = re.compile(r'^d(x_rdata|xdot|\w+?)d(\w+?)(?:_explicit)?$')
@dataclass
class _FunctionInfo:
"""Information on a model-specific generated C++ function
:ivar ode_arguments: argument list of the ODE function. input variables should be
``const``.
:ivar dae_arguments: argument list of the DAE function, if different from ODE
function. input variables should be ``const``.
:ivar return_type: the return type of the function
:ivar assume_pow_positivity:
identifies the functions on which ``assume_pow_positivity`` will have
an effect when specified during model generation. generally these are
functions that are used for solving the ODE, where negative values may
negatively affect convergence of the integration algorithm
:ivar sparse:
specifies whether the result of this function will be stored in sparse
format. sparse format means that the function will only return an
array of nonzero values and not a full matrix.
:ivar generate_body:
indicates whether a model-specific implementation is to be generated
:ivar body:
the actual function body. will be filled later
"""
ode_arguments: str = ''
dae_arguments: str = ''
return_type: str = 'void'
assume_pow_positivity: bool = False
sparse: bool = False
generate_body: bool = True
body: str = ''
def arguments(self, ode: bool = True) -> str:
"""Get the arguments for the ODE or DAE function"""
if ode or not self.dae_arguments:
return self.ode_arguments
return self.dae_arguments
# Information on a model-specific generated C++ function
# prototype for generated C++ functions, keys are the names of functions
functions = {
'Jy':
_FunctionInfo(
'realtype *Jy, const int iy, const realtype *p, '
'const realtype *k, const realtype *y, const realtype *sigmay, '
'const realtype *my'
),
'dJydsigma':
_FunctionInfo(
'realtype *dJydsigma, const int iy, const realtype *p, '
'const realtype *k, const realtype *y, const realtype *sigmay, '
'const realtype *my'
),
'dJydy':
_FunctionInfo(
'realtype *dJydy, const int iy, const realtype *p, '
'const realtype *k, const realtype *y, '
'const realtype *sigmay, const realtype *my',
sparse=True
),
'Jz':
_FunctionInfo(
'realtype *Jz, const int iz, const realtype *p, const realtype *k, '
'const realtype *z, const realtype *sigmaz, const realtype *mz'
),
'dJzdsigma':
_FunctionInfo(
'realtype *dJzdsigma, const int iz, const realtype *p, '
'const realtype *k, const realtype *z, const realtype *sigmaz, '
'const realtype *mz'
),
'dJzdz':
_FunctionInfo(
'realtype *dJzdz, const int iz, const realtype *p, '
'const realtype *k, const realtype *z, const realtype *sigmaz, '
'const double *mz',
),
'Jrz':
_FunctionInfo(
'realtype *Jrz, const int iz, const realtype *p, '
'const realtype *k, const realtype *rz, const realtype *sigmaz'
),
'dJrzdsigma':
_FunctionInfo(
'realtype *dJrzdsigma, const int iz, const realtype *p, '
'const realtype *k, const realtype *rz, const realtype *sigmaz'
),
'dJrzdz':
_FunctionInfo(
'realtype *dJrzdz, const int iz, const realtype *p, '
'const realtype *k, const realtype *rz, const realtype *sigmaz',
),
'root':
_FunctionInfo(
'realtype *root, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *tcl'
),
'dwdp':
_FunctionInfo(
'realtype *dwdp, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w, const realtype *tcl, const realtype *dtcldp',
assume_pow_positivity=True, sparse=True
),
'dwdx':
_FunctionInfo(
'realtype *dwdx, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w, const realtype *tcl',
assume_pow_positivity=True, sparse=True
),
'dwdw':
_FunctionInfo(
'realtype *dwdw, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w, const realtype *tcl',
assume_pow_positivity=True, sparse=True
),
'dxdotdw':
_FunctionInfo(
'realtype *dxdotdw, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w',
'realtype *dxdotdw, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *dx, const realtype *w',
assume_pow_positivity=True, sparse=True
),
'dxdotdx_explicit':
_FunctionInfo(
'realtype *dxdotdx_explicit, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const realtype *w',
'realtype *dxdotdx_explicit, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const realtype *dx, const realtype *w',
assume_pow_positivity=True, sparse=True
),
'dxdotdp_explicit':
_FunctionInfo(
'realtype *dxdotdp_explicit, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const realtype *w',
'realtype *dxdotdp_explicit, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const realtype *dx, const realtype *w',
assume_pow_positivity=True, sparse=True
),
'dydx':
_FunctionInfo(
'realtype *dydx, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w, const realtype *dwdx',
),
'dydp':
_FunctionInfo(
'realtype *dydp, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const int ip, const realtype *w, const realtype *tcl, '
'const realtype *dtcldp',
),
'dzdx':
_FunctionInfo(
'realtype *dzdx, const int ie, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h',
),
'dzdp':
_FunctionInfo(
'realtype *dzdp, const int ie, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const int ip',
),
'drzdx':
_FunctionInfo(
'realtype *drzdx, const int ie, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h',
),
'drzdp':
_FunctionInfo(
'realtype *drzdp, const int ie, const realtype t, '
'const realtype *x, const realtype *p, const realtype *k, '
'const realtype *h, const int ip',
),
'dsigmaydy':
_FunctionInfo(
'realtype *dsigmaydy, const realtype t, const realtype *p, '
'const realtype *k, const realtype *y'
),
'dsigmaydp':
_FunctionInfo(
'realtype *dsigmaydp, const realtype t, const realtype *p, '
'const realtype *k, const realtype *y, const int ip',
),
'sigmay':
_FunctionInfo(
'realtype *sigmay, const realtype t, const realtype *p, '
'const realtype *k, const realtype *y',
),
'dsigmazdp':
_FunctionInfo(
'realtype *dsigmazdp, const realtype t, const realtype *p,'
' const realtype *k, const int ip',
),
'sigmaz':
_FunctionInfo(
'realtype *sigmaz, const realtype t, const realtype *p, '
'const realtype *k',
),
'sroot':
_FunctionInfo(
'realtype *stau, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *sx, const int ip, const int ie, '
'const realtype *tcl',
generate_body=False
),
'drootdt':
_FunctionInfo(generate_body=False),
'drootdt_total':
_FunctionInfo(generate_body=False),
'drootdp':
_FunctionInfo(generate_body=False),
'drootdx':
_FunctionInfo(generate_body=False),
'stau':
_FunctionInfo(
'realtype *stau, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *tcl, const realtype *sx, const int ip, '
'const int ie'
),
'deltax':
_FunctionInfo(
'double *deltax, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const int ie, const realtype *xdot, const realtype *xdot_old'
),
'ddeltaxdx':
_FunctionInfo(generate_body=False),
'ddeltaxdt':
_FunctionInfo(generate_body=False),
'ddeltaxdp':
_FunctionInfo(generate_body=False),
'deltasx':
_FunctionInfo(
'realtype *deltasx, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w, const int ip, const int ie, '
'const realtype *xdot, const realtype *xdot_old, '
'const realtype *sx, const realtype *stau, const realtype *tcl'
),
'w':
_FunctionInfo(
'realtype *w, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, '
'const realtype *h, const realtype *tcl',
assume_pow_positivity=True
),
'x0':
_FunctionInfo(
'realtype *x0, const realtype t, const realtype *p, '
'const realtype *k'
),
'x0_fixedParameters':
_FunctionInfo(
'realtype *x0_fixedParameters, const realtype t, '
'const realtype *p, const realtype *k, '
'gsl::span<const int> reinitialization_state_idxs',
),
'sx0':
_FunctionInfo(
'realtype *sx0, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const int ip',
),
'sx0_fixedParameters':
_FunctionInfo(
'realtype *sx0_fixedParameters, const realtype t, '
'const realtype *x0, const realtype *p, const realtype *k, '
'const int ip, gsl::span<const int> reinitialization_state_idxs',
),
'xdot':
_FunctionInfo(
'realtype *xdot, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *w',
'realtype *xdot, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h, '
'const realtype *dx, const realtype *w',
assume_pow_positivity=True
),
'xdot_old':
_FunctionInfo(generate_body=False),
'y':
_FunctionInfo(
'realtype *y, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, '
'const realtype *h, const realtype *w',
),
'x_rdata':
_FunctionInfo(
'realtype *x_rdata, const realtype *x, const realtype *tcl, '
'const realtype *p, const realtype *k'
),
'total_cl':
_FunctionInfo(
'realtype *total_cl, const realtype *x_rdata, '
'const realtype *p, const realtype *k'
),
'dtotal_cldp':
_FunctionInfo(
'realtype *dtotal_cldp, const realtype *x_rdata, '
'const realtype *p, const realtype *k, const int ip'
),
'dtotal_cldx_rdata':
_FunctionInfo(
'realtype *dtotal_cldx_rdata, const realtype *x_rdata, '
'const realtype *p, const realtype *k, const realtype *tcl',
sparse=True
),
'x_solver':
_FunctionInfo('realtype *x_solver, const realtype *x_rdata'),
'dx_rdatadx_solver':
_FunctionInfo(
'realtype *dx_rdatadx_solver, const realtype *x, '
'const realtype *tcl, const realtype *p, const realtype *k',
sparse=True
),
'dx_rdatadp':
_FunctionInfo(
'realtype *dx_rdatadp, const realtype *x, '
'const realtype *tcl, const realtype *p, const realtype *k, '
'const int ip'
),
'dx_rdatadtcl':
_FunctionInfo(
'realtype *dx_rdatadtcl, const realtype *x, '
'const realtype *tcl, const realtype *p, const realtype *k',
sparse=True
),
'z':
_FunctionInfo(
'realtype *z, const int ie, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h'
),
'rz':
_FunctionInfo(
'realtype *rz, const int ie, const realtype t, const realtype *x, '
'const realtype *p, const realtype *k, const realtype *h'
),
}
# list of sparse functions
sparse_functions = [
func_name for func_name, func_info in functions.items()
if func_info.sparse
]
# list of nobody functions
nobody_functions = [
func_name for func_name, func_info in functions.items()
if not func_info.generate_body
]
# list of sensitivity functions
sensi_functions = [
func_name for func_name, func_info in functions.items()
if 'const int ip' in func_info.arguments()
]
# list of sensitivity functions
sparse_sensi_functions = [
func_name for func_name, func_info in functions.items()
if 'const int ip' not in func_info.arguments()
and func_name.endswith('dp') or func_name.endswith('dp_explicit')
]
# list of event functions
event_functions = [
func_name for func_name, func_info in functions.items()
if 'const int ie' in func_info.arguments() and
'const int ip' not in func_info.arguments()
]
event_sensi_functions = [
func_name for func_name, func_info in functions.items()
if 'const int ie' in func_info.arguments() and
'const int ip' in func_info.arguments()
]
# list of multiobs functions
multiobs_functions = [
func_name for func_name, func_info in functions.items()
if 'const int iy' in func_info.arguments()
or 'const int iz' in func_info.arguments()
]
# list of equations that have ids which may not be unique
non_unique_id_symbols = [
'x_rdata', 'y'
]
# custom c++ function replacements
CUSTOM_FUNCTIONS = [
{'sympy': 'polygamma',
'c++': 'boost::math::polygamma',
'include': '#include <boost/math/special_functions/polygamma.hpp>',
'build_hint': 'Using polygamma requires libboost-math header files.'
},
{'sympy': 'Heaviside',
'c++': 'amici::heaviside'},
{'sympy': 'DiracDelta',
'c++': 'amici::dirac'}
]
# python log manager
logger = get_logger(__name__, logging.ERROR)
[docs]def var_in_function_signature(name: str, varname: str, ode: bool) -> bool:
"""
Checks if the values for a symbolic variable is passed in the signature
of a function
:param name:
name of the function
:param varname:
name of the symbolic variable
:param ode:
whether to check the ODE or DAE signature
:return:
boolean indicating whether the variable occurs in the function
signature
"""
return name in functions \
and re.search(
rf'const (realtype|double) \*{varname}[0]*(,|$)+',
functions[name].arguments(ode=ode)
)
# defines the type of some attributes in DEModel
symbol_to_type = {
SymbolId.SPECIES: DifferentialState,
SymbolId.ALGEBRAIC_STATE: AlgebraicState,
SymbolId.ALGEBRAIC_EQUATION: AlgebraicEquation,
SymbolId.PARAMETER: Parameter,
SymbolId.FIXED_PARAMETER: Constant,
SymbolId.OBSERVABLE: Observable,
SymbolId.EVENT_OBSERVABLE: EventObservable,
SymbolId.SIGMAY: SigmaY,
SymbolId.SIGMAZ: SigmaZ,
SymbolId.LLHY: LogLikelihoodY,
SymbolId.LLHZ: LogLikelihoodZ,
SymbolId.LLHRZ: LogLikelihoodRZ,
SymbolId.EXPRESSION: Expression,
SymbolId.EVENT: Event
}
[docs]@log_execution_time('running smart_jacobian', logger)
def smart_jacobian(
eq: sp.MutableDenseMatrix,
sym_var: sp.MutableDenseMatrix
) -> sp.MutableSparseMatrix:
"""
Wrapper around symbolic jacobian with some additional checks that reduce
computation time for large matrices
:param eq:
equation
:param sym_var:
differentiation variable
:return:
jacobian of eq wrt sym_var
"""
nrow = eq.shape[0]
ncol = sym_var.shape[0]
if (
not min(eq.shape)
or not min(sym_var.shape)
or smart_is_zero_matrix(eq)
or smart_is_zero_matrix(sym_var)
):
return sp.MutableSparseMatrix(nrow, ncol, dict())
# preprocess sparsity pattern
elements = (
(i, j, a, b)
for i, a in enumerate(eq)
for j, b in enumerate(sym_var)
if a.has(b)
)
if (n_procs := int(os.environ.get("AMICI_IMPORT_NPROCS", 1))) == 1:
# serial
return sp.MutableSparseMatrix(nrow, ncol,
dict(starmap(_jacobian_element, elements))
)
# parallel
from multiprocessing import get_context
# "spawn" should avoid potential deadlocks occurring with fork
# see e.g. https://stackoverflow.com/a/66113051
ctx = get_context('spawn')
with ctx.Pool(n_procs) as p:
mapped = p.starmap(_jacobian_element, elements)
return sp.MutableSparseMatrix(nrow, ncol, dict(mapped))
[docs]@log_execution_time('running smart_multiply', logger)
def smart_multiply(
x: Union[sp.MutableDenseMatrix, sp.MutableSparseMatrix],
y: sp.MutableDenseMatrix
) -> Union[sp.MutableDenseMatrix, sp.MutableSparseMatrix]:
"""
Wrapper around symbolic multiplication with some additional checks that
reduce computation time for large matrices
:param x:
educt 1
:param y:
educt 2
:return:
product
"""
if not x.shape[0] or not y.shape[1] or smart_is_zero_matrix(x) or \
smart_is_zero_matrix(y):
return sp.zeros(x.shape[0], y.shape[1])
return x.multiply(y)
[docs]def smart_is_zero_matrix(x: Union[sp.MutableDenseMatrix,
sp.MutableSparseMatrix]) -> bool:
"""A faster implementation of sympy's is_zero_matrix
Avoids repeated indexer type checks and double iteration to distinguish
False/None. Found to be about 100x faster for large matrices.
:param x: Matrix to check
"""
if isinstance(x, sp.MutableDenseMatrix):
return all(xx.is_zero is True for xx in x.flat())
if isinstance(x, list):
return all(smart_is_zero_matrix(xx) for xx in x)
return x.nnz() == 0
def _default_simplify(x):
"""Default simplification applied in DEModel"""
# We need this as a free function instead of a lambda to have it picklable
# for parallel simplification
return sp.powsimp(x, deep=True)
[docs]class DEModel:
"""
Defines a Differential Equation as set of ModelQuantities.
This class provides general purpose interfaces to compute arbitrary
symbolic derivatives that are necessary for model simulation or
sensitivity computation.
:ivar _differential_states:
list of differential state variables
:ivar _algebraic_states:
list of algebraic state variables
:ivar _observables:
list of observables
:ivar _event_observables:
list of event observables
:ivar _sigma_ys:
list of sigmas for observables
:ivar _sigma_zs:
list of sigmas for event observables
:ivar _parameters:
list of parameters
:ivar _log_likelihood_ys:
list of loglikelihoods for observables
:ivar _log_likelihood_zs:
list of loglikelihoods for event observables
:ivar _log_likelihood_rzs:
list of loglikelihoods for event observable regularizations
:ivar _expressions:
list of expressions instances
:ivar _conservation_laws:
list of conservation laws
:ivar _symboldim_funs:
define functions that compute model dimensions, these
are functions as the underlying symbolic expressions have not been
populated at compile time
:ivar _eqs:
carries symbolic formulas of the symbolic variables of the model
:ivar _sparseeqs:
carries linear list of all symbolic formulas for sparsified
variables
:ivar _vals:
carries numeric values of symbolic identifiers of the symbolic
variables of the model
:ivar _names:
carries the names of symbolic identifiers of the symbolic variables
of the model
:ivar _syms:
carries symbolic identifiers of the symbolic variables of the
model
:ivar _sparsesyms:
carries linear list of all symbolic identifiers for sparsified
variables
:ivar _colptrs:
carries column pointers for sparsified variables. See
SUNMatrixContent_Sparse definition in ``sunmatrix/sunmatrix_sparse.h``
:ivar _rowvals:
carries row values for sparsified variables. See
SUNMatrixContent_Sparse definition in ``sunmatrix/sunmatrix_sparse.h``
:ivar _equation_prototype:
defines the attribute from which an equation should be generated via
list comprehension (see :meth:`OEModel._generate_equation`)
:ivar _variable_prototype:
defines the attribute from which a variable should be generated via
list comprehension (see :meth:`DEModel._generate_symbol`)
:ivar _value_prototype:
defines the attribute from which a value should be generated via
list comprehension (see :meth:`DEModel._generate_value`)
:ivar _total_derivative_prototypes:
defines how a total derivative equation is computed for an equation,
key defines the name and values should be arguments for
:meth:`DEModel.totalDerivative`
:ivar _lock_total_derivative:
add chainvariables to this set when computing total derivative from
a partial derivative call to enforce a partial derivative in the
next recursion. prevents infinite recursion
:ivar _simplify:
If not None, this function will be used to simplify symbolic
derivative expressions. Receives sympy expressions as only argument.
To apply multiple simplifications, wrap them in a lambda expression.
:ivar _x0_fixedParameters_idx:
Index list of subset of states for which x0_fixedParameters was
computed
:ivar _w_recursion_depth:
recursion depth in w, quantified as nilpotency of dwdw
:ivar _has_quadratic_nllh:
whether all observables have a gaussian noise model, i.e. whether
res and FIM make sense.
:ivar _code_printer:
Code printer to generate C++ code
:ivar _z2event:
list of event indices for each event observable
"""
[docs] def __init__(self, verbose: Optional[Union[bool, int]] = False,
simplify: Optional[Callable] = _default_simplify,
cache_simplify: bool = False):
"""
Create a new DEModel instance.
:param verbose:
verbosity level for logging, True/False default to
``logging.DEBUG``/``logging.ERROR``
:param simplify:
see :meth:`DEModel._simplify`
:param cache_simplify:
Whether to cache calls to the simplify method. Can e.g. decrease
import times for models with events.
"""
self._differential_states: List[DifferentialState] = []
self._algebraic_states: List[AlgebraicState] = []
self._algebraic_equations: List[AlgebraicEquation] = []
self._observables: List[Observable] = []
self._event_observables: List[EventObservable] = []
self._sigma_ys: List[SigmaY] = []
self._sigma_zs: List[SigmaZ] = []
self._parameters: List[Parameter] = []
self._constants: List[Constant] = []
self._log_likelihood_ys: List[LogLikelihoodY] = []
self._log_likelihood_zs: List[LogLikelihoodZ] = []
self._log_likelihood_rzs: List[LogLikelihoodRZ] = []
self._expressions: List[Expression] = []
self._conservation_laws: List[ConservationLaw] = []
self._events: List[Event] = []
self._symboldim_funs: Dict[str, Callable[[], int]] = {
'sx': self.num_states_solver,
'v': self.num_states_solver,
'vB': self.num_states_solver,
'xB': self.num_states_solver,
'sigmay': self.num_obs,
'sigmaz': self.num_eventobs,
}
self._eqs: Dict[str, Union[sp.Matrix, sp.SparseMatrix,
List[Union[sp.Matrix, sp.SparseMatrix]]]] = dict()
self._sparseeqs: Dict[str, Union[sp.Matrix, List[sp.Matrix]]] = dict()
self._vals: Dict[str, List[sp.Expr]] = dict()
self._names: Dict[str, List[str]] = dict()
self._syms: Dict[str, Union[sp.Matrix, List[sp.Matrix]]] = dict()
self._sparsesyms: Dict[str, Union[List[str], List[List[str]]]] = dict()
self._colptrs: Dict[str, Union[List[int], List[List[int]]]] = dict()
self._rowvals: Dict[str, Union[List[int], List[List[int]]]] = dict()
self._equation_prototype: Dict[str, Callable] = {
'total_cl': self.conservation_laws,
'x0': self.states,
'y': self.observables,
'Jy': self.log_likelihood_ys,
'Jz': self.log_likelihood_zs,
'Jrz': self.log_likelihood_rzs,
'w': self.expressions,
'root': self.events,
'sigmay': self.sigma_ys,
'sigmaz': self.sigma_zs
}
self._variable_prototype: Dict[str, Callable] = {
'tcl': self.conservation_laws,
'x_rdata': self.states,
'y': self.observables,
'z': self.event_observables,
'p': self.parameters,
'k': self.constants,
'w': self.expressions,
'sigmay': self.sigma_ys,
'sigmaz': self.sigma_zs,
'h': self.events,
}
self._value_prototype: Dict[str, Callable] = {
'p': self.parameters,
'k': self.constants,
}
self._total_derivative_prototypes: \
Dict[str, Dict[str, Union[str, List[str]]]] = {
'sroot': {
'eq': 'root',
'chainvars': ['x'],
'var': 'p',
'dxdz_name': 'sx',
},
}
self._lock_total_derivative: List[str] = list()
self._simplify: Callable = simplify
if cache_simplify and simplify is not None:
def cached_simplify(
expr: sp.Expr,
_simplified: Dict[str, sp.Expr] = {},
_simplify: Callable = simplify,
) -> sp.Expr:
"""Speed up expression simplification with caching.
NB: This can decrease model import times for models that have
many repeated expressions during C++ file generation.
For example, this can be useful for models with events.
However, for other models, this may increase model import
times.
:param expr:
The SymPy expression.
:param _simplified:
The cache.
:param _simplify:
The simplification method.
:return:
The simplified expression.
"""
expr_str = repr(expr)
if expr_str not in _simplified:
_simplified[expr_str] = _simplify(expr)
return _simplified[expr_str]
self._simplify = cached_simplify
self._x0_fixedParameters_idx: Union[None, Sequence[int]]
self._w_recursion_depth: int = 0
self._has_quadratic_nllh: bool = True
set_log_level(logger, verbose)
self._code_printer = AmiciCxxCodePrinter()
for fun in CUSTOM_FUNCTIONS:
self._code_printer.known_functions[fun['sympy']] = fun['c++']
[docs] def differential_states(self) -> List[DifferentialState]:
"""Get all differential states."""
return self._differential_states
[docs] def algebraic_states(self) -> List[AlgebraicState]:
"""Get all algebraic states."""
return self._algebraic_states
[docs] def observables(self) -> List[Observable]:
"""Get all observables."""
return self._observables
[docs] def parameters(self) -> List[Parameter]:
"""Get all parameters."""
return self._parameters
[docs] def constants(self) -> List[Constant]:
"""Get all constants."""
return self._constants
[docs] def expressions(self) -> List[Expression]:
"""Get all expressions."""
return self._expressions
[docs] def events(self) -> List[Event]:
"""Get all events."""
return self._events
[docs] def event_observables(self) -> List[EventObservable]:
"""Get all event observables."""
return self._event_observables
[docs] def sigma_ys(self) -> List[SigmaY]:
"""Get all observable sigmas."""
return self._sigma_ys
[docs] def sigma_zs(self) -> List[SigmaZ]:
"""Get all event observable sigmas."""
return self._sigma_zs
[docs] def conservation_laws(self) -> List[ConservationLaw]:
"""Get all conservation laws."""
return self._conservation_laws
[docs] def log_likelihood_ys(self) -> List[LogLikelihoodY]:
"""Get all observable log likelihoodss."""
return self._log_likelihood_ys
[docs] def log_likelihood_zs(self) -> List[LogLikelihoodZ]:
"""Get all event observable log likelihoods."""
return self._log_likelihood_zs
[docs] def log_likelihood_rzs(self) -> List[LogLikelihoodRZ]:
"""Get all event observable regularization log likelihoods."""
return self._log_likelihood_rzs
[docs] def is_ode(self) -> bool:
"""Check if model is ODE model."""
return len(self._algebraic_equations) == 0
[docs] def states(self) -> List[State]:
"""Get all states."""
return self._differential_states + self._algebraic_states
[docs] @log_execution_time('importing SbmlImporter', logger)
def import_from_sbml_importer(
self,
si: 'sbml_import.SbmlImporter',
compute_cls: Optional[bool] = True
) -> None:
"""
Imports a model specification from a
:class:`amici.sbml_import.SbmlImporter` instance.
:param si:
imported SBML model
:param compute_cls:
whether to compute conservation laws
"""
# get symbolic expression from SBML importers
symbols = copy.copy(si.symbols)
# assemble fluxes and add them as expressions to the model
assert len(si.flux_ids) == len(si.flux_vector)
fluxes = [generate_flux_symbol(ir, name=flux_id)
for ir, flux_id in enumerate(si.flux_ids)]
# correct time derivatives for compartment changes
def transform_dxdt_to_concentration(species_id, dxdt):
"""
Produces the appropriate expression for the first derivative of a
species with respect to time, for species that reside in
compartments with a constant volume, or a volume that is defined by
an assignment or rate rule.
:param species_id:
The identifier of the species (generated in "sbml_import.py").
:param dxdt:
The element-wise product of the row in the stoichiometric
matrix that corresponds to the species (row x_index) and the
flux (kinetic laws) vector. Ignored in the case of rate rules.
"""
# The derivation of the below return expressions can be found in
# the documentation. They are found by rearranging
# $\frac{d}{dt} (vx) = Sw$ for $\frac{dx}{dt}$, where $v$ is the
# vector of species compartment volumes, $x$ is the vector of
# species concentrations, $S$ is the stoichiometric matrix, and $w$
# is the flux vector. The conditional below handles the cases of
# species in (i) compartments with a rate rule, (ii) compartments
# with an assignment rule, and (iii) compartments with a constant
# volume, respectively.
species = si.symbols[SymbolId.SPECIES][species_id]
comp = species['compartment']
if comp in si.symbols[SymbolId.SPECIES]:
dv_dt = si.symbols[SymbolId.SPECIES][comp]['dt']
xdot = (dxdt - dv_dt * species_id) / comp
return xdot
elif comp in si.compartment_assignment_rules:
v = si.compartment_assignment_rules[comp]
# we need to flatten out assignments in the compartment in
# order to ensure that we catch all species dependencies
v = smart_subs_dict(v, si.symbols[SymbolId.EXPRESSION],
'value')
dv_dt = v.diff(si.amici_time_symbol)
# we may end up with a time derivative of the compartment
# volume due to parameter rate rules
comp_rate_vars = [p for p in v.free_symbols
if p in si.symbols[SymbolId.SPECIES]]
for var in comp_rate_vars:
dv_dt += \
v.diff(var) * si.symbols[SymbolId.SPECIES][var]['dt']
dv_dx = v.diff(species_id)
xdot = (dxdt - dv_dt * species_id) / (dv_dx * species_id + v)
return xdot
elif comp in si.symbols[SymbolId.ALGEBRAIC_STATE]:
raise SBMLException(
f'Species {species_id} is in a compartment {comp} that is'
f' defined by an algebraic equation. This is not'
f' supported.'
)
else:
v = si.compartments[comp]
if v == 1.0:
return dxdt
return dxdt / v
# create dynamics without respecting conservation laws first
dxdt = smart_multiply(si.stoichiometric_matrix,
MutableDenseMatrix(fluxes))
for ix, ((species_id, species), formula) in enumerate(zip(
symbols[SymbolId.SPECIES].items(),
dxdt
)):
# rate rules and amount species don't need to be updated
if 'dt' in species:
continue
if species['amount']:
species['dt'] = formula
else:
species['dt'] = transform_dxdt_to_concentration(species_id,
formula)
# create all basic components of the DE model and add them.
for symbol_name in symbols:
# transform dict of lists into a list of dicts
args = ['name', 'identifier']
if symbol_name == SymbolId.SPECIES:
args += ['dt', 'init']
elif symbol_name == SymbolId.ALGEBRAIC_STATE:
args += ['init']
else:
args += ['value']
if symbol_name == SymbolId.EVENT:
args += ['state_update', 'initial_value']
elif symbol_name == SymbolId.OBSERVABLE:
args += ['transformation']
elif symbol_name == SymbolId.EVENT_OBSERVABLE:
args += ['event']
comp_kwargs = [
{
'identifier': var_id,
**{k: v for k, v in var.items() if k in args}
}
for var_id, var in symbols[symbol_name].items()
]
for comp_kwarg in comp_kwargs:
self.add_component(symbol_to_type[symbol_name](**comp_kwarg))
# add fluxes as expressions, this needs to happen after base
# expressions from symbols have been parsed
for flux_id, flux in zip(fluxes, si.flux_vector):
self.add_component(Expression(
identifier=flux_id,
name=str(flux_id),
value=flux
))
# process conservation laws
if compute_cls:
si.process_conservation_laws(self)
# fill in 'self._sym' based on prototypes and components in ode_model
self.generate_basic_variables()
self._has_quadratic_nllh = all(
llh['dist'] in ['normal', 'lin-normal', 'log-normal',
'log10-normal']
for llh in si.symbols[SymbolId.LLHY].values()
)
[docs] def add_component(self, component: ModelQuantity,
insert_first: Optional[bool] = False) -> None:
"""
Adds a new ModelQuantity to the model.
:param component:
model quantity to be added
:param insert_first:
whether to add quantity first or last, relevant when components
may refer to other components of the same type.
"""
if type(component) not in {
Observable, Expression, Parameter, Constant, DifferentialState,
AlgebraicState, AlgebraicEquation,
LogLikelihoodY, LogLikelihoodZ, LogLikelihoodRZ,
SigmaY, SigmaZ, ConservationLaw, Event, EventObservable
}:
raise ValueError(f'Invalid component type {type(component)}')
component_list = getattr(
self, '_' + '_'.join(
s.lower() for s in re.split(r"([A-Z][^A-Z]+)",
type(component).__name__) if s
) + 's'
)
if insert_first:
component_list.insert(0, component)
else:
component_list.append(component)
[docs] def add_conservation_law(self,
state: sp.Symbol,
total_abundance: sp.Symbol,
coefficients: Dict[sp.Symbol, sp.Expr]) -> None:
r"""
Adds a new conservation law to the model. A conservation law is defined
by the conserved quantity :math:`T = \sum_i(a_i * x_i)`, where
:math:`a_i` are coefficients and :math:`x_i` are different state
variables.
:param state:
symbolic identifier of the state that should be replaced by
the conservation law (:math:`x_j`)
:param total_abundance:
symbolic identifier of the total abundance (:math:`T/a_j`)
:param coefficients:
Dictionary of coefficients {x_i: a_i}
"""
try:
ix = next(filter(lambda is_s: is_s[1].get_id() == state,
enumerate(self._differential_states)))[0]
except StopIteration:
raise ValueError(f'Specified state {state} was not found in the '
f'model states.')
state_id = self._differential_states[ix].get_id()
# \sum_{i≠j}(a_i * x_i)/a_j
target_expression = sp.Add(*(
c_i*x_i for x_i, c_i in coefficients.items() if x_i != state
)) / coefficients[state]
# x_j = T/a_j - \sum_{i≠j}(a_i * x_i)/a_j
state_expr = total_abundance - target_expression
# T/a_j = \sum_{i≠j}(a_i * x_i)/a_j + x_j
abundance_expr = target_expression + state_id
self.add_component(
Expression(state_id, str(state_id), state_expr),
insert_first=True
)
cl = ConservationLaw(
total_abundance, f'total_{state_id}', abundance_expr,
coefficients, state_id
)
self.add_component(cl)
self._differential_states[ix].set_conservation_law(cl)
[docs] def num_states_rdata(self) -> int:
"""
Number of states.
:return:
number of state variable symbols
"""
return len(self.sym('x_rdata'))
[docs] def num_states_solver(self) -> int:
"""
Number of states after applying conservation laws.
:return:
number of state variable symbols
"""
return len(self.sym('x'))
[docs] def num_cons_law(self) -> int:
"""
Number of conservation laws.
:return:
number of conservation laws
"""
return self.num_states_rdata() - self.num_states_solver()
[docs] def num_state_reinits(self) -> int:
"""
Number of solver states which would be reinitialized after
preequilibration
:return:
number of state variable symbols with reinitialization
"""
reinit_states = self.eq('x0_fixedParameters')
solver_states = self.eq('x_solver')
return sum(ix in solver_states for ix in reinit_states)
[docs] def num_obs(self) -> int:
"""
Number of Observables.
:return:
number of observable symbols
"""
return len(self.sym('y'))
[docs] def num_eventobs(self) -> int:
"""
Number of Event Observables.
:return:
number of event observable symbols
"""
return len(self.sym('z'))
[docs] def num_const(self) -> int:
"""
Number of Constants.
:return:
number of constant symbols
"""
return len(self.sym('k'))
[docs] def num_par(self) -> int:
"""
Number of Parameters.
:return:
number of parameter symbols
"""
return len(self.sym('p'))
[docs] def num_expr(self) -> int:
"""
Number of Expressions.
:return:
number of expression symbols
"""
return len(self.sym('w'))
[docs] def num_events(self) -> int:
"""
Number of Events.
:return:
number of event symbols (length of the root vector in AMICI)
"""
return len(self.sym('h'))
[docs] def sym(self, name: str) -> sp.Matrix:
"""
Returns (and constructs if necessary) the identifiers for a symbolic
entity.
:param name:
name of the symbolic variable
:return:
matrix of symbolic identifiers
"""
if name not in self._syms:
self._generate_symbol(name)
return self._syms[name]
[docs] def sparsesym(self, name: str, force_generate: bool = True) -> List[str]:
"""
Returns (and constructs if necessary) the sparsified identifiers for
a sparsified symbolic variable.
:param name:
name of the symbolic variable
:param force_generate:
whether the symbols should be generated if not available
:return:
linearized Matrix containing the symbolic identifiers
"""
if name not in sparse_functions:
raise ValueError(f'{name} is not marked as sparse')
if name not in self._sparsesyms and force_generate:
self._generate_sparse_symbol(name)
return self._sparsesyms.get(name, [])
[docs] def eq(self, name: str) -> sp.Matrix:
"""
Returns (and constructs if necessary) the formulas for a symbolic
entity.
:param name:
name of the symbolic variable
:return:
matrix of symbolic formulas
"""
if name not in self._eqs:
dec = log_execution_time(f'computing {name}', logger)
dec(self._compute_equation)(name)
return self._eqs[name]
[docs] def sparseeq(self, name) -> sp.Matrix:
"""
Returns (and constructs if necessary) the sparsified formulas for a
sparsified symbolic variable.
:param name:
name of the symbolic variable
:return:
linearized matrix containing the symbolic formulas
"""
if name not in sparse_functions:
raise ValueError(f'{name} is not marked as sparse')
if name not in self._sparseeqs:
self._generate_sparse_symbol(name)
return self._sparseeqs[name]
[docs] def colptrs(self, name: str) -> Union[List[sp.Number],
List[List[sp.Number]]]:
"""
Returns (and constructs if necessary) the column pointers for
a sparsified symbolic variable.
:param name:
name of the symbolic variable
:return:
list containing the column pointers
"""
if name not in sparse_functions:
raise ValueError(f'{name} is not marked as sparse')
if name not in self._sparseeqs:
self._generate_sparse_symbol(name)
return self._colptrs[name]
[docs] def rowvals(self, name: str) -> Union[List[sp.Number],
List[List[sp.Number]]]:
"""
Returns (and constructs if necessary) the row values for a
sparsified symbolic variable.
:param name:
name of the symbolic variable
:return:
list containing the row values
"""
if name not in sparse_functions:
raise ValueError(f'{name} is not marked as sparse')
if name not in self._sparseeqs:
self._generate_sparse_symbol(name)
return self._rowvals[name]
[docs] def val(self, name: str) -> List[sp.Number]:
"""
Returns (and constructs if necessary) the numeric values of a
symbolic entity
:param name:
name of the symbolic variable
:return:
list containing the numeric values
"""
if name not in self._vals:
self._generate_value(name)
return self._vals[name]
[docs] def name(self, name: str) -> List[str]:
"""
Returns (and constructs if necessary) the names of a symbolic
variable
:param name:
name of the symbolic variable
:return:
list of names
"""
if name not in self._names:
self._generate_name(name)
return self._names[name]
[docs] def free_symbols(self) -> Set[sp.Basic]:
"""
Returns list of free symbols that appear in RHS and initial
conditions.
"""
return set(chain.from_iterable(
state.get_free_symbols()
for state in self.states() + self.algebraic_equations()
))
def _generate_symbol(self, name: str) -> None:
"""
Generates the symbolic identifiers for a symbolic variable
:param name:
name of the symbolic variable
"""
if name in self._variable_prototype:
components = self._variable_prototype[name]()
self._syms[name] = sp.Matrix([
comp.get_id()
for comp in components
])
if name == 'y':
self._syms['my'] = sp.Matrix([
comp.get_measurement_symbol()
for comp in components
])
if name == 'z':
self._syms['mz'] = sp.Matrix([
comp.get_measurement_symbol()
for comp in components
])
self._syms['rz'] = sp.Matrix([
comp.get_regularization_symbol()
for comp in components
])
return
elif name == 'x':
self._syms[name] = sp.Matrix([
state.get_id()
for state in self.states()
if not state.has_conservation_law()
])
return
elif name == 'xdot':
self._syms[name] = sp.Matrix([
f'd{x.get_id()}dt' if self.is_ode() else f'de_{ix}'
for ix, x in enumerate(self._differential_states)
if not x.has_conservation_law()
] + [
f'ae_{ix}'
for ix in range(len(self._algebraic_equations))
])
return
elif name == 'dx':
self._syms[name] = sp.Matrix([
f'd{state.get_id()}dt'
for state in self.states()
if not state.has_conservation_law()
])
return
elif name == 'sx0':
self._syms[name] = sp.Matrix([
f's{state.get_id()}_0'
for state in self.states()
if not state.has_conservation_law()
])
return
elif name == 'sx_rdata':
self._syms[name] = sp.Matrix([
f'sx_rdata_{i}'
for i in range(len(self.states()))
])
return
elif name == 'dtcldp':
# check, whether the CL consists of only one state. Then,
# sensitivities drop out, otherwise generate symbols
self._syms[name] = sp.Matrix([
[sp.Symbol(f's{strip_pysb(tcl.get_id())}__'
f'{strip_pysb(par.get_id())}', real=True)
for par in self._parameters]
if self.conservation_law_has_multispecies(tcl)
else [0] * self.num_par()
for tcl in self._conservation_laws
])
return
elif name == 'xdot_old':
length = len(self.eq('xdot'))
elif name in sparse_functions:
self._generate_sparse_symbol(name)
return
elif name in self._symboldim_funs:
length = self._symboldim_funs[name]()
elif name == 'stau':
length = self.eq(name)[0].shape[1]
elif name in sensi_functions:
length = self.eq(name).shape[0]
else:
length = len(self.eq(name))
self._syms[name] = sp.Matrix([
sp.Symbol(f'{name}{0 if name == "stau" else i}', real=True)
for i in range(length)
])
[docs] def generate_basic_variables(self) -> None:
"""
Generates the symbolic identifiers for all variables in
``DEModel._variable_prototype``
"""
# We need to process events and Heaviside functions in the ``DEModel`,
# before adding it to DEExporter
self.parse_events()
for var in self._variable_prototype:
if var not in self._syms:
self._generate_symbol(var)
self._generate_symbol('x')
[docs] def parse_events(self) -> None:
"""
This function checks the right-hand side for roots of Heaviside
functions or events, collects the roots, removes redundant roots,
and replaces the formulae of the found roots by identifiers of AMICI's
Heaviside function implementation in the right-hand side
"""
# Track all roots functions in the right-hand side
roots = copy.deepcopy(self._events)
for state in self._differential_states:
state.set_dt(self._process_heavisides(state.get_dt(), roots))
for expr in self._expressions:
expr.set_val(self._process_heavisides(expr.get_val(), roots))
# remove all possible Heavisides from roots, which may arise from
# the substitution of `'w'` in `_collect_heaviside_roots`
for root in roots:
root.set_val(self._process_heavisides(root.get_val(), roots))
# Now add the found roots to the model components
for root in roots:
# skip roots of SBML events, as these have already been added
if root in self._events:
continue
# add roots of heaviside functions
self.add_component(root)
[docs] def get_appearance_counts(self, idxs: List[int]) -> List[int]:
"""
Counts how often a state appears in the time derivative of
another state and expressions for a subset of states
:param idxs:
list of state indices for which counts are to be computed
:return:
list of counts for the states ordered according to the provided
indices
"""
free_symbols_dt = list(itertools.chain.from_iterable(
[
str(symbol)
for symbol in state.get_dt().free_symbols
]
for state in self.states()
))
free_symbols_expr = list(itertools.chain.from_iterable(
[
str(symbol)
for symbol in expr.get_val().free_symbols
]
for expr in self._expressions
))
return [
free_symbols_dt.count(str(self._differential_states[idx].get_id()))
+
free_symbols_expr.count(str(self._differential_states[idx].get_id()))
for idx in idxs
]
def _generate_sparse_symbol(self, name: str) -> None:
"""
Generates the sparse symbolic identifiers, symbolic identifiers,
sparse equations, column pointers and row values for a symbolic
variable
:param name:
name of the symbolic variable
"""
matrix = self.eq(name)
if match_deriv := DERIVATIVE_PATTERN.match(name):
eq = match_deriv[1]
var = match_deriv[2]
rownames = self.sym(eq)
colnames = self.sym(var)
if name == 'dJydy':
# One entry per y-slice
self._colptrs[name] = []
self._rowvals[name] = []
self._sparseeqs[name] = []
self._sparsesyms[name] = []
self._syms[name] = []
for iy in range(self.num_obs()):
symbol_col_ptrs, symbol_row_vals, sparse_list, symbol_list, \
sparse_matrix = self._code_printer.csc_matrix(
matrix[iy, :], rownames=rownames, colnames=colnames,
identifier=iy)
self._colptrs[name].append(symbol_col_ptrs)
self._rowvals[name].append(symbol_row_vals)
self._sparseeqs[name].append(sparse_list)
self._sparsesyms[name].append(symbol_list)
self._syms[name].append(sparse_matrix)
else:
symbol_col_ptrs, symbol_row_vals, sparse_list, symbol_list, \
sparse_matrix = self._code_printer.csc_matrix(
matrix, rownames=rownames, colnames=colnames,
pattern_only=name in nobody_functions
)
self._colptrs[name] = symbol_col_ptrs
self._rowvals[name] = symbol_row_vals
self._sparseeqs[name] = sparse_list
self._sparsesyms[name] = symbol_list
self._syms[name] = sparse_matrix
def _compute_equation(self, name: str) -> None:
"""
Computes the symbolic formula for a symbolic variable
:param name:
name of the symbolic variable
"""
# replacement ensures that we don't have to adapt name in abstract
# model and keep backwards compatibility with matlab
match_deriv = DERIVATIVE_PATTERN.match(
re.sub(r'dJ(y|z|rz)dsigma', r'dJ\1dsigma\1', name)
.replace('sigmarz', 'sigmaz')
.replace('dJrzdz', 'dJrzdrz')
)
time_symbol = sp.Matrix([symbol_with_assumptions('t')])
if name in self._equation_prototype:
self._equation_from_components(name, self._equation_prototype[name]())
elif name in self._total_derivative_prototypes:
args = self._total_derivative_prototypes[name]
args['name'] = name
self._lock_total_derivative += args['chainvars']
self._total_derivative(**args)
for cv in args['chainvars']:
self._lock_total_derivative.remove(cv)
elif name == 'xdot':
if self.is_ode():
self._eqs[name] = sp.Matrix([
state.get_dt() for state in self._differential_states
if not state.has_conservation_law()
])
else:
self._eqs[name] = sp.Matrix([
x.get_dt() - dx
for x, dx in zip(
(s for s in self._differential_states
if not s.has_conservation_law()),
self.sym('dx')
)
] + [
eq.get_val()
for eq in self._algebraic_equations
])
elif name == 'x_rdata':
self._eqs[name] = sp.Matrix([
state.get_x_rdata()
for state in self.states()
])
elif name == 'x_solver':
self._eqs[name] = sp.Matrix([
state.get_id()
for state in self.states()
if not state.has_conservation_law()
])
elif name == 'sx_solver':
self._eqs[name] = sp.Matrix([
self.sym('sx_rdata')[ix]
for ix, state in enumerate(self.states())
if not state.has_conservation_law()
])
elif name == 'sx0':
self._derivative(name[1:], 'p', name=name)
elif name == 'sx0_fixedParameters':
# deltax = -x+x0_fixedParameters if x0_fixedParameters>0 else 0
# deltasx = -sx+dx0_fixed_parametersdx*sx+dx0_fixedParametersdp
# if x0_fixedParameters>0 else 0
# sx0_fixedParameters = sx+deltasx =
# dx0_fixed_parametersdx*sx+dx0_fixedParametersdp
self._eqs[name] = smart_jacobian(
self.eq('x0_fixedParameters'), self.sym('p')
)
dx0_fixed_parametersdx = smart_jacobian(
self.eq('x0_fixedParameters'), self.sym('x')
)
if not smart_is_zero_matrix(dx0_fixed_parametersdx):
if isinstance(self._eqs[name], ImmutableDenseMatrix):
self._eqs[name] = MutableDenseMatrix(self._eqs[name])
tmp = smart_multiply(dx0_fixed_parametersdx, self.sym('sx0'))
for ip in range(self._eqs[name].shape[1]):
self._eqs[name][:, ip] += tmp
elif name == 'x0_fixedParameters':
k = self.sym('k')
self._x0_fixedParameters_idx = [
ix
for ix, eq in enumerate(self.eq('x0'))
if any(sym in eq.free_symbols for sym in k)
]
eq = self.eq('x0')
self._eqs[name] = sp.Matrix([eq[ix] for ix in
self._x0_fixedParameters_idx])
elif name == 'dtotal_cldx_rdata':
x_rdata = self.sym('x_rdata')
self._eqs[name] = sp.Matrix(
[
[cl.get_ncoeff(xr) for xr in x_rdata]
for cl in self._conservation_laws
]
)
elif name == 'dtcldx':
# this is always zero
self._eqs[name] = \
sp.zeros(self.num_cons_law(), self.num_states_solver())
elif name == 'dtcldp':
# force symbols
self._eqs[name] = self.sym(name)
elif name == 'dx_rdatadx_solver':
if self.num_cons_law():
x_solver = self.sym('x')
self._eqs[name] = sp.Matrix(
[
[state.get_dx_rdata_dx_solver(xs) for xs in x_solver]
for state in self.states()
]
)
else:
# so far, dx_rdatadx_solver is only required for sx_rdata
# in case of no conservation laws, C++ code will directly use
# sx, we don't need this
self._eqs[name] = \
sp.zeros(self.num_states_rdata(),
self.num_states_solver())
elif name == 'dx_rdatadp':
if self.num_cons_law():
self._eqs[name] = smart_jacobian(self.eq('x_rdata'),
self.sym('p'))
else:
# so far, dx_rdatadp is only required for sx_rdata
# in case of no conservation laws, C++ code will directly use
# sx, we don't need this
self._eqs[name] = \
sp.zeros(self.num_states_rdata(),
self.num_par())
elif name == 'dx_rdatadtcl':
self._eqs[name] = smart_jacobian(self.eq('x_rdata'),
self.sym('tcl'))
elif name == 'dxdotdx_explicit':
# force symbols
self._derivative('xdot', 'x', name=name)
elif name == 'dxdotdp_explicit':
# force symbols
self._derivative('xdot', 'p', name=name)
elif name == 'drootdt':
self._eqs[name] = smart_jacobian(self.eq('root'), time_symbol)
elif name == 'drootdt_total':
# backsubstitution of optimized right-hand side terms into RHS
# calling subs() is costly. Due to looping over events though, the
# following lines are only evaluated if a model has events
w_sorted = \
toposort_symbols(dict(zip(self.sym('w'), self.eq('w'))))
tmp_xdot = smart_subs_dict(self.eq('xdot'), w_sorted)
self._eqs[name] = self.eq('drootdt')
if self.num_states_solver():
self._eqs[name] += smart_multiply(self.eq('drootdx'), tmp_xdot)
elif name == 'deltax':
# fill boluses for Heaviside functions, as empty state updates
# would cause problems when writing the function file later
event_eqs = []
for event in self._events:
if event._state_update is None:
event_eqs.append(sp.zeros(self.num_states_solver(), 1))
else:
event_eqs.append(event._state_update)
self._eqs[name] = event_eqs
elif name == 'z':
event_observables = [
sp.zeros(self.num_eventobs(), 1)
for _ in self._events
]
event_ids = [
e.get_id() for e in self._events
]
# TODO: get rid of this stupid 1-based indexing as soon as we can
# the matlab interface
z2event = [
event_ids.index(event_obs.get_event()) + 1
for event_obs in self._event_observables
]
for (iz, ie), event_obs in zip(enumerate(z2event),
self._event_observables):
event_observables[ie-1][iz] = event_obs.get_val()
self._eqs[name] = event_observables
self._z2event = z2event
elif name in ['ddeltaxdx', 'ddeltaxdp', 'ddeltaxdt', 'dzdp', 'dzdx']:
if match_deriv[2] == 't':
var = time_symbol
else:
var = self.sym(match_deriv[2])
self._eqs[name] = [
smart_jacobian(self.eq(match_deriv[1])[ie], var)
for ie in range(self.num_events())
]
if name == 'dzdx':
for ie in range(self.num_events()):
dtaudx = -self.eq('drootdx')[ie, :] / \
self.eq('drootdt_total')[ie]
for iz in range(self.num_eventobs()):
if ie != self._z2event[iz]-1:
continue
dzdt = sp.diff(self.eq('z')[ie][iz], time_symbol)
self._eqs[name][ie][iz, :] += dzdt * dtaudx
elif name in ['rz', 'drzdx', 'drzdp']:
eq_events = []
for ie in range(self.num_events()):
val = sp.zeros(
self.num_eventobs(),
1 if name == 'rz' else len(self.sym(match_deriv[2]))
)
# match event observables to root function
for iz in range(self.num_eventobs()):
if ie == self._z2event[iz]-1:
val[iz, :] = self.eq(name.replace('rz', 'root'))[ie, :]
eq_events.append(val)
self._eqs[name] = eq_events
elif name == 'stau':
self._eqs[name] = [
-self.eq('sroot')[ie, :] / self.eq('drootdt_total')[ie]
if not self.eq('drootdt_total')[ie].is_zero else
sp.zeros(*self.eq('sroot')[ie, :].shape)
for ie in range(self.num_events())
]
elif name == 'deltasx':
event_eqs = []
for ie, event in enumerate(self._events):
tmp_eq = sp.zeros(self.num_states_solver(), self.num_par())
# need to check if equations are zero since we are using
# symbols
if not smart_is_zero_matrix(self.eq('stau')[ie]):
tmp_eq += smart_multiply(
(self.sym('xdot_old') - self.sym('xdot')),
self.sym('stau').T)
# only add deltax part if there is state update
if event._state_update is not None:
# partial derivative for the parameters
tmp_eq += self.eq('ddeltaxdp')[ie]
# initial part of chain rule state variables
tmp_dxdp = self.sym('sx') * sp.ones(1, self.num_par())
# need to check if equations are zero since we are using
# symbols
if not smart_is_zero_matrix(self.eq('stau')[ie]):
# chain rule for the time point
tmp_eq += smart_multiply(self.eq('ddeltaxdt')[ie],
self.sym('stau').T)
# additional part of chain rule state variables
# This part only works if we use self.eq('xdot')
# instead of self.sym('xdot'). Not immediately clear
# why that is.
tmp_dxdp += smart_multiply(self.eq('xdot'),
self.sym('stau').T)
# finish chain rule for the state variables
tmp_eq += smart_multiply(self.eq('ddeltaxdx')[ie],
tmp_dxdp)
event_eqs.append(tmp_eq)
self._eqs[name] = event_eqs
elif name == 'xdot_old':
# force symbols
self._eqs[name] = self.sym(name)
elif name == 'dwdx':
x = self.sym('x')
self._eqs[name] = sp.Matrix([
[-cl.get_ncoeff(xs) for xs in x]
# the insert first in ode_model._add_conservation_law() means
# that we need to reverse the order here
for cl in reversed(self._conservation_laws)
]).col_join(smart_jacobian(self.eq('w')[self.num_cons_law():, :],
x))
elif match_deriv:
self._derivative(match_deriv[1], match_deriv[2], name)
else:
raise ValueError(f'Unknown equation {name}')
if name == 'root':
# Events are processed after the model has been set up.
# Equations are there, but symbols for roots must be added
self.sym('h')
if name in {'Jy', 'dydx'}:
# do not transpose if we compute the partial derivative as part of
# a total derivative
if not len(self._lock_total_derivative):
self._eqs[name] = self._eqs[name].transpose()
if name in {'dzdx', 'drzdx'}:
self._eqs[name] = [
e.T for e in self._eqs[name]
]
if self._simplify:
dec = log_execution_time(f'simplifying {name}', logger)
if isinstance(self._eqs[name], list):
self._eqs[name] = [
dec(_parallel_applyfunc)(sub_eq, self._simplify)
for sub_eq in self._eqs[name]
]
else:
self._eqs[name] = dec(_parallel_applyfunc)(self._eqs[name],
self._simplify)
[docs] def sym_names(self) -> List[str]:
"""
Returns a list of names of generated symbolic variables
:return:
list of names
"""
return list(self._syms.keys())
def _derivative(self, eq: str, var: str, name: str = None) -> None:
"""
Creates a new symbolic variable according to a derivative
:param eq:
name of the symbolic variable that defines the formula
:param var:
name of the symbolic variable that defines the identifiers
with respect to which the derivatives are to be computed
:param name:
name of resulting symbolic variable, default is ``d{eq}d{var}``
"""
if not name:
name = f'd{eq}d{var}'
ignore_chainrule = {
('xdot', 'p'): 'w', # has generic implementation in c++ code
('xdot', 'x'): 'w', # has generic implementation in c++ code
('w', 'w'): 'tcl', # dtcldw = 0
('w', 'x'): 'tcl', # dtcldx = 0
}
# automatically detect chainrule
chainvars = [
cv for cv in ['w', 'tcl']
if var_in_function_signature(eq, cv, self.is_ode())
and cv not in self._lock_total_derivative
and var != cv
and min(self.sym(cv).shape)
and (
(eq, var) not in ignore_chainrule
or ignore_chainrule[(eq, var)] != cv
)
]
if len(chainvars):
self._lock_total_derivative += chainvars
self._total_derivative(name, eq, chainvars, var)
for cv in chainvars:
self._lock_total_derivative.remove(cv)
return
# partial derivative
sym_eq = self.eq(eq).transpose() if eq == 'Jy' else self.eq(eq)
sym_var = self.sym(var)
derivative = smart_jacobian(sym_eq, sym_var)
self._eqs[name] = derivative
# compute recursion depth based on nilpotency of jacobian. computing
# nilpotency can be done more efficiently on numerical sparsity pattern
if name == 'dwdw':
nonzeros = np.asarray(
derivative.applyfunc(lambda x: int(not x.is_zero))
).astype(np.int64)
recursion = nonzeros.copy()
if max(recursion.shape):
while recursion.max():
recursion = recursion.dot(nonzeros)
self._w_recursion_depth += 1
if self._w_recursion_depth > len(sym_eq):
raise RuntimeError(
'dwdw is not nilpotent. Something, somewhere went '
'terribly wrong. Please file a bug report at '
'https://github.com/AMICI-dev/AMICI/issues and '
'attach this model.'
)
if name == 'dydw' and not smart_is_zero_matrix(derivative):
dwdw = self.eq('dwdw')
# h(k) = d{eq}dw*dwdw^k* (k=1)
h = smart_multiply(derivative, dwdw)
while not smart_is_zero_matrix(h):
self._eqs[name] += h
# h(k+1) = d{eq}dw*dwdw^(k+1) = h(k)*dwdw
h = smart_multiply(h, dwdw)
def _total_derivative(self, name: str, eq: str, chainvars: List[str],
var: str, dydx_name: str = None,
dxdz_name: str = None) -> None:
"""
Creates a new symbolic variable according to a total derivative
using the chain rule
:param name:
name of resulting symbolic variable
:param eq:
name of the symbolic variable that defines the formula
:param chainvars:
names of the symbolic variable that define the
identifiers with respect to which the chain rules are applied
:param var:
name of the symbolic variable that defines the identifiers
with respect to which the derivatives are to be computed
:param dydx_name:
defines the name of the symbolic variable that
defines the derivative of the ``eq`` with respect to ``chainvar``,
default is ``d{eq}d{chainvar}``
:param dxdz_name:
defines the name of the symbolic variable that
defines the derivative of the ``chainvar`` with respect to ``var``,
default is d{chainvar}d{var}
"""
# compute total derivative according to chainrule
# Dydz = dydx*dxdz + dydz
# initialize with partial derivative dydz without chain rule
self._eqs[name] = self.sym_or_eq(name, f'd{eq}d{var}')
if not isinstance(self._eqs[name], sp.Symbol):
# if not a Symbol, create a copy using sympy API
# NB deepcopy does not work safely, see sympy issue #7672
self._eqs[name] = self._eqs[name].copy()
for chainvar in chainvars:
if dydx_name is None:
dydx_name = f'd{eq}d{chainvar}'
if dxdz_name is None:
dxdz_name = f'd{chainvar}d{var}'
dydx = self.sym_or_eq(name, dydx_name)
dxdz = self.sym_or_eq(name, dxdz_name)
# Save time for large models if one multiplicand is zero,
# which is not checked for by sympy
if not smart_is_zero_matrix(dydx) and not \
smart_is_zero_matrix(dxdz):
dydx_times_dxdz = smart_multiply(dydx, dxdz)
if dxdz.shape[1] == 1 and \
self._eqs[name].shape[1] != dxdz.shape[1]:
for iz in range(self._eqs[name].shape[1]):
self._eqs[name][:, iz] += dydx_times_dxdz
else:
self._eqs[name] += dydx_times_dxdz
[docs] def sym_or_eq(self, name: str, varname: str) -> sp.Matrix:
"""
Returns symbols or equations depending on whether a given
variable appears in the function signature or not.
:param name:
name of function for which the signature should be checked
:param varname:
name of the variable which should be contained in the
function signature
:return:
the variable symbols if the variable is part of the signature and
the variable equations otherwise.
"""
# dwdx and dwdp will be dynamically computed and their ordering
# within a column may differ from the initialization of symbols here,
# so those are not safe to use. Not removing them from signature as
# this would break backwards compatibility.
if var_in_function_signature(name, varname, self.is_ode()) \
and varname not in ['dwdx', 'dwdp']:
return self.sym(varname)
else:
return self.eq(varname)
def _multiplication(self, name: str, x: str, y: str,
transpose_x: Optional[bool] = False,
sign: Optional[int] = 1):
"""
Creates a new symbolic variable according to a multiplication
:param name:
name of resulting symbolic variable, default is ``d{eq}d{var}``
:param x:
name of the symbolic variable that defines the first factor
:param y:
name of the symbolic variable that defines the second factor
:param transpose_x:
indicates whether the first factor should be
transposed before multiplication
:param sign:
defines the sign of the product, should be +1 or -1
"""
if sign not in [-1, 1]:
raise TypeError(f'sign must be +1 or -1, was {sign}')
variables = {
varname: self.sym(varname)
if var_in_function_signature(name, varname, self.is_ode())
else self.eq(varname)
for varname in [x, y]
}
xx = variables[x].transpose() if transpose_x else variables[x]
yy = variables[y]
self._eqs[name] = sign * smart_multiply(xx, yy)
def _equation_from_components(self, name: str,
components: List[ModelQuantity]) -> None:
"""
Generates the formulas of a symbolic variable from the attributes
:param name:
name of resulting symbolic variable
:param component:
name of the attribute
"""
self._eqs[name] = sp.Matrix(
[comp.get_val() for comp in components]
)
[docs] def get_conservation_laws(self) -> List[Tuple[sp.Symbol, sp.Expr]]:
"""Returns a list of states with conservation law set
:return:
list of state identifiers
"""
return [
(state.get_id(), state.get_x_rdata())
for state in self.states()
if state.has_conservation_law()
]
def _generate_value(self, name: str) -> None:
"""
Generates the numeric values of a symbolic variable from value
prototypes
:param name:
name of resulting symbolic variable
"""
if name in self._value_prototype:
components = self._value_prototype[name]()
else:
raise ValueError(f'No values for {name}')
self._vals[name] = [comp.get_val()
for comp in components]
def _generate_name(self, name: str) -> None:
"""
Generates the names of a symbolic variable from variable prototypes or
equation prototypes
:param name:
name of resulting symbolic variable
"""
if name in self._variable_prototype:
components = self._variable_prototype[name]()
elif name in self._equation_prototype:
components = self._equation_prototype[name]()
else:
raise ValueError(f'No names for {name}')
self._names[name] = [comp.get_name()
for comp in components]
[docs] def state_has_fixed_parameter_initial_condition(self, ix: int) -> bool:
"""
Checks whether the state at specified index has a fixed parameter
initial condition
:param ix:
state index
:return:
boolean indicating if any of the initial condition free
variables is contained in the model constants
"""
ic = self.states()[ix].get_val()
if not isinstance(ic, sp.Basic):
return False
return any(
fp in (c.get_id() for c in self._constants)
for fp in ic.free_symbols
)
[docs] def state_has_conservation_law(self, ix: int) -> bool:
"""
Checks whether the state at specified index has a conservation
law set
:param ix:
state index
:return:
boolean indicating if conservation_law is not None
"""
return self.states()[ix].has_conservation_law()
[docs] def get_solver_indices(self) -> Dict[int, int]:
"""
Returns a mapping that maps rdata species indices to solver indices
:return:
dictionary mapping rdata species indices to solver indices
"""
solver_index = {}
ix_solver = 0
for ix in range(len(self.states())):
if self.state_has_conservation_law(ix):
continue
solver_index[ix] = ix_solver
ix_solver += 1
return solver_index
[docs] def state_is_constant(self, ix: int) -> bool:
"""
Checks whether the temporal derivative of the state is zero
:param ix:
state index
:return:
boolean indicating if constant over time
"""
state = self.states()[ix]
if isinstance(state, AlgebraicState):
return False
return state.get_dt() == 0.0
[docs] def conservation_law_has_multispecies(self,
tcl: ConservationLaw) -> bool:
"""
Checks whether a conservation law has multiple species or it just
defines one constant species
:param tcl:
conservation law
:return:
boolean indicating if conservation_law is not None
"""
state_set = set(self.sym('x_rdata'))
n_species = len(state_set.intersection(tcl.get_val().free_symbols))
return n_species > 1
def _expr_is_time_dependent(self, expr: sp.Expr) -> bool:
"""Determine whether an expression is time-dependent.
:param expr:
The expression.
:returns:
Whether the expression is time-dependent.
"""
# `expr.free_symbols` will be different to `self._states.keys()`, so
# it's easier to compare as `str`.
expr_syms = {str(sym) for sym in expr.free_symbols}
# Check if the time variable is in the expression.
if 't' in expr_syms:
return True
# Check if any time-dependent states are in the expression.
state_syms = [str(sym) for sym in self.states()]
return any(
not self.state_is_constant(state_syms.index(state))
for state in expr_syms.intersection(state_syms)
)
def _get_unique_root(
self,
root_found: sp.Expr,
roots: List[Event],
) -> Union[sp.Symbol, None]:
"""
Collects roots of Heaviside functions and events and stores them in
the roots list. It checks for redundancy to not store symbolically
equivalent root functions more than once.
:param root_found:
equation of the root function
:param roots:
list of already known root functions with identifier
:returns:
unique identifier for root, or ``None`` if the root is not
time-dependent
"""
if not self._expr_is_time_dependent(root_found):
return None
for root in roots:
if sp.simplify(root_found - root.get_val()) == 0:
return root.get_id()
# create an event for a new root function
root_symstr = f'Heaviside_{len(roots)}'
roots.append(Event(
identifier=sp.Symbol(root_symstr),
name=root_symstr,
value=root_found,
state_update=None,
))
return roots[-1].get_id()
def _collect_heaviside_roots(
self,
args: Sequence[sp.Expr],
) -> List[sp.Expr]:
"""
Recursively checks an expression for the occurrence of Heaviside
functions and return all roots found
:param args:
args attribute of the expanded expression
:returns:
root functions that were extracted from Heaviside function
arguments
"""
root_funs = []
for arg in args:
if arg.func == sp.Heaviside:
root_funs.append(arg.args[0])
elif arg.has(sp.Heaviside):
root_funs.extend(self._collect_heaviside_roots(arg.args))
# substitute 'w' expressions into root expressions now, to avoid
# rewriting 'root.cpp' and 'stau.cpp' headers
# to include 'w.h'
w_sorted = toposort_symbols(dict(zip(
[expr.get_id() for expr in self._expressions],
[expr.get_val() for expr in self._expressions],
)))
root_funs = [
r.subs(w_sorted)
for r in root_funs
]
return root_funs
def _process_heavisides(
self,
dxdt: sp.Expr,
roots: List[Event],
) -> sp.Expr:
"""
Parses the RHS of a state variable, checks for Heaviside functions,
collects unique roots functions that can be tracked by SUNDIALS and
replaces Heaviside Functions by amici helper variables that will be
updated based on SUNDIALS root tracking.
:param dxdt:
right-hand side of state variable
:param roots:
list of known root functions with identifier
:returns:
dxdt with Heaviside functions replaced by amici helper variables
"""
# expanding the rhs will in general help to collect the same
# heaviside function
dt_expanded = dxdt.expand()
# track all the old Heaviside expressions in tmp_roots_old
# replace them later by the new expressions
heavisides = []
# run through the expression tree and get the roots
tmp_roots_old = self._collect_heaviside_roots(dt_expanded.args)
for tmp_old in tmp_roots_old:
# we want unique identifiers for the roots
tmp_new = self._get_unique_root(tmp_old, roots)
# `tmp_new` is None if the root is not time-dependent.
if tmp_new is None:
continue
# For Heavisides, we need to add the negative function as well
self._get_unique_root(sp.sympify(- tmp_old), roots)
heavisides.append((sp.Heaviside(tmp_old), tmp_new))
if heavisides:
# only apply subs if necessary
for heaviside_sympy, heaviside_amici in heavisides:
dxdt = dxdt.subs(heaviside_sympy, heaviside_amici)
return dxdt
[docs]class DEExporter:
"""
The DEExporter class generates AMICI C++ files for a model as
defined in symbolic expressions.
:ivar model:
DE definition
:ivar verbose:
more verbose output if True
:ivar assume_pow_positivity:
if set to true, a special pow function is
used to avoid problems with state variables that may become negative
due to numerical errors
:ivar compiler:
distutils/setuptools compiler selection to build the Python extension
:ivar functions:
carries C++ function signatures and other specifications
:ivar model_name:
name of the model that will be used for compilation
:ivar model_path:
path to the generated model specific files
:ivar model_swig_path:
path to the generated swig files
:ivar allow_reinit_fixpar_initcond:
indicates whether reinitialization of
initial states depending on fixedParameters is allowed for this model
:ivar _build_hints:
If the given model uses special functions, this set contains hints for
model building.
:ivar generate_sensitivity_code:
Specifies whether code for sensitivity computation is to be generated
.. note::
When importing large models (several hundreds of species or
parameters), import time can potentially be reduced by using multiple
CPU cores. This is controlled by setting the ``AMICI_IMPORT_NPROCS``
environment variable to the number of parallel processes that are to be
used (default: 1). Note that for small models this may (slightly)
increase import times.
"""
[docs] def __init__(
self,
de_model: DEModel,
outdir: Optional[Union[Path, str]] = None,
verbose: Optional[Union[bool, int]] = False,
assume_pow_positivity: Optional[bool] = False,
compiler: Optional[str] = None,
allow_reinit_fixpar_initcond: Optional[bool] = True,
generate_sensitivity_code: Optional[bool] = True,
model_name: Optional[str] = 'model'
):
"""
Generate AMICI C++ files for the DE provided to the constructor.
:param de_model:
DE model definition
:param outdir:
see :meth:`amici.ode_export.DEExporter.set_paths`
:param verbose:
verbosity level for logging, ``True``/``False`` default to
:data:`logging.Error`/:data:`logging.DEBUG`
:param assume_pow_positivity:
if set to true, a special pow function is
used to avoid problems with state variables that may become
negative due to numerical errors
:param compiler: distutils/setuptools compiler selection to build the
python extension
:param allow_reinit_fixpar_initcond:
see :class:`amici.ode_export.DEExporter`
:param generate_sensitivity_code:
specifies whether code required for sensitivity computation will be
generated
:param model_name:
name of the model to be used during code generation
"""
set_log_level(logger, verbose)
self.verbose: bool = logger.getEffectiveLevel() <= logging.DEBUG
self.assume_pow_positivity: bool = assume_pow_positivity
self.compiler: str = compiler
self.model_path: str = ''
self.model_swig_path: str = ''
self.set_name(model_name)
self.set_paths(outdir)
# Signatures and properties of generated model functions (see
# include/amici/model.h for details)
self.model: DEModel = de_model
# To only generate a subset of functions, apply subselection here
self.functions: Dict[str, _FunctionInfo] = copy.deepcopy(functions)
self.allow_reinit_fixpar_initcond: bool = allow_reinit_fixpar_initcond
self._build_hints = set()
self.generate_sensitivity_code: bool = generate_sensitivity_code
[docs] @log_execution_time('generating cpp code', logger)
def generate_model_code(self) -> None:
"""
Generates the native C++ code for the loaded model and a Matlab
script that can be run to compile a mex file from the C++ code
"""
with _monkeypatched(sp.Pow, '_eval_derivative',
_custom_pow_eval_derivative):
self._prepare_model_folder()
self._generate_c_code()
self._generate_m_code()
[docs] @log_execution_time('compiling cpp code', logger)
def compile_model(self) -> None:
"""
Compiles the generated code it into a simulatable module
"""
self._compile_c_code(compiler=self.compiler,
verbose=self.verbose)
def _prepare_model_folder(self) -> None:
"""
Create model directory or remove all files if the output directory
already exists.
"""
os.makedirs(self.model_path, exist_ok=True)
for file in os.listdir(self.model_path):
file_path = os.path.join(self.model_path, file)
if os.path.isfile(file_path):
os.remove(file_path)
def _generate_c_code(self) -> None:
"""
Create C++ code files for the model based on
:attribute:`DEExporter.model`.
"""
for func_name, func_info in self.functions.items():
if func_name in sensi_functions + sparse_sensi_functions and \
not self.generate_sensitivity_code:
continue
if func_info.generate_body:
dec = log_execution_time(f'writing {func_name}.cpp', logger)
dec(self._write_function_file)(func_name)
if func_name in sparse_functions and func_info.body:
self._write_function_index(func_name, 'colptrs')
self._write_function_index(func_name, 'rowvals')
for name in self.model.sym_names():
# only generate for those that have nontrivial implementation,
# check for both basic variables (not in functions) and function
# computed values
if (name in self.functions
and not self.functions[name].body
and name not in nobody_functions) \
or (name not in self.functions and
len(self.model.sym(name)) == 0):
continue
self._write_index_files(name)
self._write_wrapfunctions_cpp()
self._write_wrapfunctions_header()
self._write_model_header_cpp()
self._write_c_make_file()
self._write_swig_files()
self._write_module_setup()
shutil.copy(CXX_MAIN_TEMPLATE_FILE,
os.path.join(self.model_path, 'main.cpp'))
def _compile_c_code(self,
verbose: Optional[Union[bool, int]] = False,
compiler: Optional[str] = None) -> None:
"""
Compile the generated model code
:param verbose:
Make model compilation verbose
:param compiler:
distutils/setuptools compiler selection to build the python
extension
"""
# setup.py assumes it is run from within the model directory
module_dir = self.model_path
script_args = [sys.executable, os.path.join(module_dir, 'setup.py')]
if verbose:
script_args.append('--verbose')
else:
script_args.append('--quiet')
script_args.extend([
'build_ext',
f'--build-lib={module_dir}',
# This is generally not required, but helps to reduce the path
# length of intermediate build files, that may easily become
# problematic on Windows, due to its ridiculous 255-character path
# length limit.
f'--build-temp={Path(module_dir, "build")}',
])
if compiler is not None:
script_args.extend([f'--compiler={compiler}'])
# distutils.core.run_setup looks nicer, but does not let us check the
# result easily
try:
result = subprocess.run(script_args,
cwd=module_dir,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
check=True)
except subprocess.CalledProcessError as e:
print(e.output.decode('utf-8'))
print("Failed building the model extension.")
if self._build_hints:
print("Note:")
print('\n'.join(self._build_hints))
raise
if verbose:
print(result.stdout.decode('utf-8'))
def _generate_m_code(self) -> None:
"""
Create a Matlab script for compiling code files to a mex file
"""
# Second order code is not yet implemented. Once this is done,
# those variables will have to be replaced by
# "self.model.<var>true()", or the corresponding "model.self.o2flag"
nxtrue_rdata = self.model.num_states_rdata()
nytrue = self.model.num_obs()
nztrue = self.model.num_eventobs()
o2flag = 0
lines = [
'% This compile script was automatically created from'
' Python SBML import.',
'% If mex compiler is set up within MATLAB, it can be run'
' from MATLAB ',
'% in order to compile a mex-file from the Python'
' generated C++ files.',
'',
f"modelName = '{self.model_name}';",
"amimodel.compileAndLinkModel(modelName, '', [], [], [], []);",
f"amimodel.generateMatlabWrapper({nxtrue_rdata}, "
f"{nytrue}, {self.model.num_par()}, "
f"{self.model.num_const()}, {nztrue}, {o2flag}, ...",
" [], ['simulate_' modelName '.m'], modelName, ...",
" 'lin', 1, 1);"
]
# write compile script (for mex)
compile_script = os.path.join(self.model_path, 'compileMexFile.m')
with open(compile_script, 'w') as fileout:
fileout.write('\n'.join(lines))
def _write_index_files(self, name: str) -> None:
"""
Write index file for a symbolic array.
:param name:
key in ``self.model._syms`` for which the respective file should
be written
"""
if name not in self.model.sym_names():
raise ValueError(f'Unknown symbolic array: {name}')
symbols = self.model.sparsesym(name) if name in sparse_functions \
else self.model.sym(name).T
# flatten multiobs
if isinstance(next(iter(symbols), None), list):
symbols = [symbol for obs in symbols for symbol in obs]
lines = []
for index, symbol in enumerate(symbols):
symbol_name = strip_pysb(symbol)
if str(symbol) == '0':
continue
if str(symbol_name) == '':
raise ValueError(f'{name} contains a symbol called ""')
lines.append(f'#define {symbol_name} {name}[{index}]')
if name == 'stau':
# we only need a single macro, as all entries have the same symbol
break
filename = os.path.join(self.model_path, f'{name}.h')
with open(filename, 'w') as fileout:
fileout.write('\n'.join(lines))
def _write_function_file(self, function: str) -> None:
"""
Generate equations and write the C++ code for the function
``function``.
:param function:
name of the function to be written (see ``self.functions``)
"""
# first generate the equations to make sure we have everything we
# need in subsequent steps
if function in sparse_functions:
equations = self.model.sparseeq(function)
elif not self.allow_reinit_fixpar_initcond \
and function == 'sx0_fixedParameters':
# Not required. Will create empty function body.
equations = sp.Matrix()
else:
equations = self.model.eq(function)
# function header
lines = [
'#include "amici/symbolic_functions.h"',
'#include "amici/defines.h"',
'#include "sundials/sundials_types.h"',
'',
'#include <gsl/gsl-lite.hpp>',
'#include <algorithm>',
''
]
func_info = self.functions[function]
# extract symbols that need definitions from signature
# don't add includes for files that won't be generated.
# Unfortunately we cannot check for `self.functions[sym].body`
# here since it may not have been generated yet.
for sym in re.findall(
r'const (?:realtype|double) \*([\w]+)[0]*(?:,|$)',
func_info.arguments(self.model.is_ode())
):
if sym not in self.model.sym_names():
continue
if sym in sparse_functions:
iszero = smart_is_zero_matrix(self.model.sparseeq(sym))
elif sym in self.functions:
iszero = smart_is_zero_matrix(self.model.eq(sym))
else:
iszero = len(self.model.sym(sym)) == 0
if iszero:
continue
lines.append(f'#include "{sym}.h"')
# include return symbols
if function in self.model.sym_names() and \
function not in non_unique_id_symbols:
lines.append(f'#include "{function}.h"')
lines.extend([
'',
'namespace amici {',
f'namespace model_{self.model_name} {{',
'',
f'{func_info.return_type} {function}_{self.model_name}'
f'({func_info.arguments(self.model.is_ode())}){{'
])
# function body
body = self._get_function_body(function, equations)
if not body:
return
if self.assume_pow_positivity and func_info.assume_pow_positivity:
pow_rx = re.compile(r'(^|\W)std::pow\(')
body = [
# execute this twice to catch cases where the ending '(' would
# be the starting (^|\W) for the following match
pow_rx.sub(r'\1amici::pos_pow(',
pow_rx.sub(r'\1amici::pos_pow(', line))
for line in body
]
self.functions[function].body = body
lines += body
lines.extend([
'}',
'',
f'}} // namespace model_{self.model_name}',
'} // namespace amici\n',
])
# check custom functions
for fun in CUSTOM_FUNCTIONS:
if 'include' in fun and any(fun['c++'] in line for line in lines):
if 'build_hint' in fun:
self._build_hints.add(fun['build_hint'])
lines.insert(0, fun['include'])
# if not body is None:
filename = os.path.join(self.model_path,
f'{function}.cpp')
with open(filename, 'w') as fileout:
fileout.write('\n'.join(lines))
def _write_function_index(self, function: str, indextype: str) -> None:
"""
Generate equations and write the C++ code for the function
``function``.
:param function:
name of the function to be written (see ``self.functions``)
:param indextype:
type of index {'colptrs', 'rowvals'}
"""
if indextype == 'colptrs':
values = self.model.colptrs(function)
setter = 'indexptrs'
elif indextype == 'rowvals':
values = self.model.rowvals(function)
setter = 'indexvals'
else:
raise ValueError('Invalid value for indextype, must be colptrs or '
f'rowvals: {indextype}')
# function signature
if function in multiobs_functions:
signature = f'(SUNMatrixWrapper &{function}, int index)'
else:
signature = f'(SUNMatrixWrapper &{function})'
lines = [
'#include "amici/sundials_matrix_wrapper.h"',
'#include "sundials/sundials_types.h"',
'',
'#include <array>',
'#include <algorithm>',
'',
'namespace amici {',
f'namespace model_{self.model_name} {{',
'',
]
# Generate static array with indices
if len(values):
static_array_name = f"{function}_{indextype}_{self.model_name}_"
if function in multiobs_functions:
# list of index vectors
lines.append(
"static constexpr std::array<std::array<sunindextype, "
f"{len(values[0])}>, {len(values)}> "
f"{static_array_name} = {{{{"
)
lines.extend([' {'
+ ', '.join(map(str, index_vector)) + '}, '
for index_vector in values])
lines.append("}};")
else:
# single index vector
lines.extend([
"static constexpr std::array<sunindextype, "
f"{len(values)}> {static_array_name} = {{",
' ' + ', '.join(map(str, values)),
"};"
])
lines.extend([
'',
f'void {function}_{indextype}_{self.model_name}{signature}{{',
])
if len(values):
if function in multiobs_functions:
lines.append(
f" {function}.set_{setter}"
f"(gsl::make_span({static_array_name}[index]));"
)
else:
lines.append(
f" {function}.set_{setter}"
f"(gsl::make_span({static_array_name}));"
)
lines.extend([
'}'
'',
f'}} // namespace model_{self.model_name}',
'} // namespace amici\n',
])
filename = f'{function}_{indextype}.cpp'
filename = os.path.join(self.model_path, filename)
with open(filename, 'w') as fileout:
fileout.write('\n'.join(lines))
def _get_function_body(
self,
function: str,
equations: sp.Matrix
) -> List[str]:
"""
Generate C++ code for body of function ``function``.
:param function:
name of the function to be written (see ``self.functions``)
:param equations:
symbolic definition of the function body
:return:
generated C++ code
"""
lines = []
if (
len(equations) == 0
or (
isinstance(equations, (sp.Matrix, sp.ImmutableDenseMatrix))
and min(equations.shape) == 0
)
):
# dJydy is a list
return lines
if not self.allow_reinit_fixpar_initcond and function in {
'sx0_fixedParameters',
'x0_fixedParameters',
}:
return lines
if function == 'sx0_fixedParameters':
# here we only want to overwrite values where x0_fixedParameters
# was applied
lines.extend([
# Keep list of indices of fixed parameters occurring in x0
" static const std::array<int, "
+ str(len(self.model._x0_fixedParameters_idx))
+ "> _x0_fixedParameters_idxs = {",
" "
+ ', '.join(str(x)
for x in self.model._x0_fixedParameters_idx),
" };",
"",
# Set all parameters that are to be reset to 0, so that the
# switch statement below only needs to handle non-zero entries
# (which usually reduces file size and speeds up
# compilation significantly).
" for(auto idx: reinitialization_state_idxs) {",
" if(std::find(_x0_fixedParameters_idxs.cbegin(), "
"_x0_fixedParameters_idxs.cend(), idx) != "
"_x0_fixedParameters_idxs.cend())\n"
" sx0_fixedParameters[idx] = 0.0;",
" }"
])
cases = {}
for ipar in range(self.model.num_par()):
expressions = []
for index, formula in zip(
self.model._x0_fixedParameters_idx,
equations[:, ipar]
):
if not formula.is_zero:
expressions.extend([
f'if(std::find('
'reinitialization_state_idxs.cbegin(), '
f'reinitialization_state_idxs.cend(), {index}) != '
'reinitialization_state_idxs.cend())',
f' {function}[{index}] = '
f'{self.model._code_printer.doprint(formula)};'
])
cases[ipar] = expressions
lines.extend(get_switch_statement('ip', cases, 1))
elif function == 'x0_fixedParameters':
for index, formula in zip(
self.model._x0_fixedParameters_idx,
equations
):
lines.append(
f' if(std::find(reinitialization_state_idxs.cbegin(), '
f'reinitialization_state_idxs.cend(), {index}) != '
'reinitialization_state_idxs.cend())\n '
f'{function}[{index}] = '
f'{self.model._code_printer.doprint(formula)};'
)
elif function in event_functions:
cases = {
ie: self.model._code_printer._get_sym_lines_array(
equations[ie], function, 0)
for ie in range(self.model.num_events())
if not smart_is_zero_matrix(equations[ie])
}
lines.extend(get_switch_statement('ie', cases, 1))
elif function in event_sensi_functions:
outer_cases = {}
for ie, inner_equations in enumerate(equations):
inner_lines = []
inner_cases = {
ipar: self.model._code_printer._get_sym_lines_array(
inner_equations[:, ipar], function, 0)
for ipar in range(self.model.num_par())
if not smart_is_zero_matrix(inner_equations[:, ipar])
}
inner_lines.extend(get_switch_statement(
'ip', inner_cases, 0))
outer_cases[ie] = copy.copy(inner_lines)
lines.extend(get_switch_statement('ie', outer_cases, 1))
elif function in sensi_functions \
and equations.shape[1] == self.model.num_par():
cases = {
ipar: self.model._code_printer._get_sym_lines_array(
equations[:, ipar], function, 0)
for ipar in range(self.model.num_par())
if not smart_is_zero_matrix(equations[:, ipar])
}
lines.extend(get_switch_statement('ip', cases, 1))
elif function in multiobs_functions:
if function == 'dJydy':
cases = {
iobs: self.model._code_printer._get_sym_lines_array(
equations[iobs], function, 0)
for iobs in range(self.model.num_obs())
if not smart_is_zero_matrix(equations[iobs])
}
else:
cases = {
iobs: self.model._code_printer._get_sym_lines_array(
equations[:, iobs], function, 0)
for iobs in range(equations.shape[1])
if not smart_is_zero_matrix(equations[:, iobs])
}
if function.startswith(('Jz', 'dJz', 'Jrz', 'dJrz')):
iterator = 'iz'
else:
iterator = 'iy'
lines.extend(get_switch_statement(iterator, cases, 1))
elif function in self.model.sym_names() \
and function not in non_unique_id_symbols:
if function in sparse_functions:
symbols = list(map(sp.Symbol, self.model.sparsesym(function)))
else:
symbols = self.model.sym(function)
lines += self.model._code_printer._get_sym_lines_symbols(
symbols, equations, function, 4)
else:
lines += self.model._code_printer._get_sym_lines_array(
equations, function, 4)
return [line for line in lines if line]
def _write_wrapfunctions_cpp(self) -> None:
"""
Write model-specific 'wrapper' file (``wrapfunctions.cpp``).
"""
template_data = {'MODELNAME': self.model_name}
apply_template(
os.path.join(amiciSrcPath, 'wrapfunctions.template.cpp'),
os.path.join(self.model_path, 'wrapfunctions.cpp'),
template_data
)
def _write_wrapfunctions_header(self) -> None:
"""
Write model-specific header file (``wrapfunctions.h``).
"""
template_data = {'MODELNAME': str(self.model_name)}
apply_template(
os.path.join(amiciSrcPath, 'wrapfunctions.template.h'),
os.path.join(self.model_path, 'wrapfunctions.h'),
template_data
)
def _write_model_header_cpp(self) -> None:
"""
Write model-specific header and cpp file (MODELNAME.{h,cpp}).
"""
model_type = 'ODE' if self.model.is_ode() else 'DAE'
tpl_data = {
'MODEL_TYPE_LOWER': model_type.lower(),
'MODEL_TYPE_UPPER': model_type,
'MODELNAME': self.model_name,
'NX_RDATA': self.model.num_states_rdata(),
'NXTRUE_RDATA': self.model.num_states_rdata(),
'NX_SOLVER': self.model.num_states_solver(),
'NXTRUE_SOLVER': self.model.num_states_solver(),
'NX_SOLVER_REINIT': self.model.num_state_reinits(),
'NY': self.model.num_obs(),
'NYTRUE': self.model.num_obs(),
'NZ': self.model.num_eventobs(),
'NZTRUE': self.model.num_eventobs(),
'NEVENT': self.model.num_events(),
'NOBJECTIVE': '1',
'NW': len(self.model.sym('w')),
'NDWDP': len(self.model.sparsesym(
'dwdp', force_generate=self.generate_sensitivity_code
)),
'NDWDX': len(self.model.sparsesym('dwdx')),
'NDWDW': len(self.model.sparsesym('dwdw')),
'NDXDOTDW': len(self.model.sparsesym('dxdotdw')),
'NDXDOTDP_EXPLICIT': len(self.model.sparsesym(
'dxdotdp_explicit',
force_generate=self.generate_sensitivity_code
)),
'NDXDOTDX_EXPLICIT': len(self.model.sparsesym(
'dxdotdx_explicit')),
'NDJYDY': 'std::vector<int>{%s}'
% ','.join(str(len(x))
for x in self.model.sparsesym('dJydy')),
'NDXRDATADXSOLVER': len(self.model.sparsesym('dx_rdatadx_solver')),
'NDXRDATADTCL': len(self.model.sparsesym('dx_rdatadtcl')),
'NDTOTALCLDXRDATA': len(self.model.sparsesym('dtotal_cldx_rdata')),
'UBW': self.model.num_states_solver(),
'LBW': self.model.num_states_solver(),
'NP': self.model.num_par(),
'NK': self.model.num_const(),
'O2MODE': 'amici::SecondOrderMode::none',
# using code printer ensures proper handling of nan/inf
'PARAMETERS': self.model._code_printer.doprint(
self.model.val('p'))[1:-1],
'FIXED_PARAMETERS': self.model._code_printer.doprint(
self.model.val('k'))[1:-1],
'PARAMETER_NAMES_INITIALIZER_LIST':
self._get_symbol_name_initializer_list('p'),
'STATE_NAMES_INITIALIZER_LIST':
self._get_symbol_name_initializer_list('x_rdata'),
'FIXED_PARAMETER_NAMES_INITIALIZER_LIST':
self._get_symbol_name_initializer_list('k'),
'OBSERVABLE_NAMES_INITIALIZER_LIST':
self._get_symbol_name_initializer_list('y'),
'OBSERVABLE_TRAFO_INITIALIZER_LIST':
'\n'.join(
f'ObservableScaling::{trafo.value}, // y[{idx}]'
for idx, trafo in enumerate(
self.model.get_observable_transformations()
)
),
'EXPRESSION_NAMES_INITIALIZER_LIST':
self._get_symbol_name_initializer_list('w'),
'PARAMETER_IDS_INITIALIZER_LIST':
self._get_symbol_id_initializer_list('p'),
'STATE_IDS_INITIALIZER_LIST':
self._get_symbol_id_initializer_list('x_rdata'),
'FIXED_PARAMETER_IDS_INITIALIZER_LIST':
self._get_symbol_id_initializer_list('k'),
'OBSERVABLE_IDS_INITIALIZER_LIST':
self._get_symbol_id_initializer_list('y'),
'EXPRESSION_IDS_INITIALIZER_LIST':
self._get_symbol_id_initializer_list('w'),
'STATE_IDXS_SOLVER_INITIALIZER_LIST':
', '.join(
str(idx)
for idx, state in enumerate(self.model.states())
if not state.has_conservation_law()
),
'REINIT_FIXPAR_INITCOND':
AmiciCxxCodePrinter.print_bool(
self.allow_reinit_fixpar_initcond),
'AMICI_VERSION_STRING': __version__,
'AMICI_COMMIT_STRING': __commit__,
'W_RECURSION_DEPTH': self.model._w_recursion_depth,
'QUADRATIC_LLH': AmiciCxxCodePrinter.print_bool(
self.model._has_quadratic_nllh),
'ROOT_INITIAL_VALUES':
', '.join(map(
lambda event: AmiciCxxCodePrinter.print_bool(
event.get_initial_value()),
self.model.events())),
'Z2EVENT':
', '.join(map(str, self.model._z2event)),
'ID':
', '.join((
str(float(isinstance(s, DifferentialState)))
for s in self.model.states()
if not s.has_conservation_law()
))
}
for func_name, func_info in self.functions.items():
if func_name in nobody_functions:
continue
if not func_info.body:
tpl_data[f'{func_name.upper()}_DEF'] = ''
if func_name in sensi_functions + sparse_sensi_functions and \
not self.generate_sensitivity_code:
impl = ''
else:
impl = get_model_override_implementation(
func_name, self.model_name, self.model.is_ode(),
nobody=True
)
tpl_data[f'{func_name.upper()}_IMPL'] = impl
if func_name in sparse_functions:
for indexfield in ['colptrs', 'rowvals']:
if func_name in sparse_sensi_functions and \
not self.generate_sensitivity_code:
impl = ''
else:
impl = get_sunindex_override_implementation(
func_name, self.model_name, indexfield,
nobody=True
)
tpl_data[f'{func_name.upper()}_{indexfield.upper()}_DEF'] \
= ''
tpl_data[f'{func_name.upper()}_{indexfield.upper()}_IMPL'] \
= impl
continue
tpl_data[f'{func_name.upper()}_DEF'] = \
get_function_extern_declaration(func_name, self.model_name,
self.model.is_ode())
tpl_data[f'{func_name.upper()}_IMPL'] = \
get_model_override_implementation(func_name, self.model_name,
self.model.is_ode())
if func_name in sparse_functions:
tpl_data[f'{func_name.upper()}_COLPTRS_DEF'] = \
get_sunindex_extern_declaration(
func_name, self.model_name, 'colptrs')
tpl_data[f'{func_name.upper()}_COLPTRS_IMPL'] = \
get_sunindex_override_implementation(
func_name, self.model_name, 'colptrs')
tpl_data[f'{func_name.upper()}_ROWVALS_DEF'] = \
get_sunindex_extern_declaration(
func_name, self.model_name, 'rowvals')
tpl_data[f'{func_name.upper()}_ROWVALS_IMPL'] = \
get_sunindex_override_implementation(
func_name, self.model_name, 'rowvals')
if self.model.num_states_solver() == self.model.num_states_rdata():
tpl_data['X_RDATA_DEF'] = ''
tpl_data['X_RDATA_IMPL'] = ''
tpl_data = {k: str(v) for k, v in tpl_data.items()}
apply_template(
os.path.join(amiciSrcPath, 'model_header.template.h'),
os.path.join(self.model_path, f'{self.model_name}.h'),
tpl_data
)
apply_template(
os.path.join(amiciSrcPath, 'model.template.cpp'),
os.path.join(self.model_path, f'{self.model_name}.cpp'),
tpl_data
)
def _get_symbol_name_initializer_list(self, name: str) -> str:
"""
Get SBML name initializer list for vector of names for the given
model entity
:param name:
any key present in ``self.model._syms``
:return:
Template initializer list of names
"""
return '\n'.join(
f'"{symbol}", // {name}[{idx}]'
for idx, symbol in enumerate(self.model.name(name))
)
def _get_symbol_id_initializer_list(self, name: str) -> str:
"""
Get C++ initializer list for vector of names for the given model
entity
:param name:
any key present in ``self.model._syms``
:return:
Template initializer list of ids
"""
return '\n'.join(
f'"{self.model._code_printer.doprint(symbol)}", // {name}[{idx}]'
for idx, symbol in enumerate(self.model.sym(name))
)
def _write_c_make_file(self):
"""Write CMake ``CMakeLists.txt`` file for this model."""
sources = '\n'.join(
f + ' ' for f in os.listdir(self.model_path)
if f.endswith(('.cpp', '.h'),) and f != 'main.cpp'
)
template_data = {'MODELNAME': self.model_name,
'SOURCES': sources,
'AMICI_VERSION': __version__}
apply_template(
MODEL_CMAKE_TEMPLATE_FILE,
Path(self.model_path, 'CMakeLists.txt'),
template_data
)
def _write_swig_files(self) -> None:
"""Write SWIG interface files for this model."""
Path(self.model_swig_path).mkdir(exist_ok=True)
template_data = {'MODELNAME': self.model_name}
apply_template(
Path(amiciSwigPath, 'modelname.template.i'),
Path(self.model_swig_path, self.model_name + '.i'),
template_data
)
shutil.copy(SWIG_CMAKE_TEMPLATE_FILE,
Path(self.model_swig_path, 'CMakeLists.txt'))
def _write_module_setup(self) -> None:
"""
Create a setuptools ``setup.py`` file for compile the model module.
"""
template_data = {'MODELNAME': self.model_name,
'AMICI_VERSION': __version__,
'PACKAGE_VERSION': '0.1.0'}
apply_template(Path(amiciModulePath, 'setup.template.py'),
Path(self.model_path, 'setup.py'),
template_data)
apply_template(Path(amiciModulePath, 'MANIFEST.template.in'),
Path(self.model_path, 'MANIFEST.in'), {})
# write __init__.py for the model module
Path(self.model_path, self.model_name).mkdir(exist_ok=True)
apply_template(
Path(amiciModulePath, '__init__.template.py'),
Path(self.model_path, self.model_name, '__init__.py'),
template_data
)
[docs] def set_paths(self, output_dir: Optional[Union[str, Path]] = None) -> None:
"""
Set output paths for the model and create if necessary
:param output_dir:
relative or absolute path where the generated model
code is to be placed. If ``None``, this will default to
``amici-{self.model_name}`` in the current working directory.
will be created if it does not exist.
"""
if output_dir is None:
output_dir = os.path.join(os.getcwd(),
f'amici-{self.model_name}')
self.model_path = os.path.abspath(output_dir)
self.model_swig_path = os.path.join(self.model_path, 'swig')
[docs] def set_name(self, model_name: str) -> None:
"""
Sets the model name
:param model_name:
name of the model (may only contain upper and lower case letters,
digits and underscores, and must not start with a digit)
"""
if not is_valid_identifier(model_name):
raise ValueError(
f"'{model_name}' is not a valid model name. "
"Model name may only contain upper and lower case letters, "
"digits and underscores, and must not start with a digit.")
self.model_name = model_name
[docs]class TemplateAmici(Template):
"""
Template format used in AMICI (see :class:`string.Template` for more
details).
:cvar delimiter:
delimiter that identifies template variables
"""
delimiter = 'TPL_'
[docs]def apply_template(source_file: Union[str, Path],
target_file: Union[str, Path],
template_data: Dict[str, str]) -> None:
"""
Load source file, apply template substitution as provided in
templateData and save as targetFile.
:param source_file:
relative or absolute path to template file
:param target_file:
relative or absolute path to output file
:param template_data:
template keywords to substitute (key is template
variable without :attr:`TemplateAmici.delimiter`)
"""
with open(source_file) as filein:
src = TemplateAmici(filein.read())
result = src.safe_substitute(template_data)
with open(target_file, 'w') as fileout:
fileout.write(result)
[docs]def get_function_extern_declaration(fun: str, name: str, ode: bool) -> str:
"""
Constructs the extern function declaration for a given function
:param fun:
function name
:param name:
model name
:param ode:
whether to generate declaration for DAE or ODE
:return:
C++ function definition string
"""
f = functions[fun]
return f'extern {f.return_type} {fun}_{name}({f.arguments(ode)});'
[docs]def get_sunindex_extern_declaration(fun: str, name: str,
indextype: str) -> str:
"""
Constructs the function declaration for an index function of a given
function
:param fun:
function name
:param name:
model name
:param indextype:
index function {'colptrs', 'rowvals'}
:return:
C++ function declaration string
"""
index_arg = ', int index' if fun in multiobs_functions else ''
return \
f'extern void {fun}_{indextype}_{name}' \
f'(SUNMatrixWrapper &{indextype}{index_arg});'
[docs]def get_model_override_implementation(fun: str, name: str, ode: bool,
nobody: bool = False) -> str:
"""
Constructs ``amici::Model::*`` override implementation for a given function
:param fun:
function name
:param name:
model name
:param nobody:
whether the function has a nontrivial implementation
:return:
C++ function implementation string
"""
func_info = functions[fun]
body = "" if nobody \
else '\n{ind8}{maybe_return}{fun}_{name}({eval_signature});{ind4}\n' \
.format(
ind4=' ' * 4,
ind8=' ' * 8,
maybe_return="" if func_info.return_type == "void" else "return ",
fun=fun,
name=name,
eval_signature=remove_argument_types(func_info.arguments(ode)),
)
return '{return_type} f{fun}({signature}) override {{{body}}}\n'.format(
return_type=func_info.return_type,
fun=fun,
signature=func_info.arguments(ode),
body=body,
)
[docs]def get_sunindex_override_implementation(fun: str, name: str,
indextype: str,
nobody: bool = False) -> str:
"""
Constructs the ``amici::Model`` function implementation for an index
function of a given function
:param fun:
function name
:param name:
model name
:param indextype:
index function {'colptrs', 'rowvals'}
:param nobody:
whether the corresponding function has a nontrivial implementation
:return:
C++ function implementation string
"""
index_arg = ', int index' if fun in multiobs_functions else ''
index_arg_eval = ', index' if fun in multiobs_functions else ''
impl = 'void f{fun}_{indextype}({signature}) override {{'
if nobody:
impl += '}}\n'
else:
impl += '{ind8}{fun}_{indextype}_{name}({eval_signature});\n{ind4}}}\n'
return impl.format(
ind4=' ' * 4,
ind8=' ' * 8,
fun=fun,
indextype=indextype,
name=name,
signature=f'SUNMatrixWrapper &{indextype}{index_arg}',
eval_signature=f'{indextype}{index_arg_eval}',
)
[docs]def remove_argument_types(signature: str) -> str:
"""
Strips argument types from a function signature
:param signature:
function signature
:return:
string that can be used to construct function calls with the same
variable names and ordering as in the function signature
"""
# remove * prefix for pointers (pointer must always be removed before
# values otherwise we will inadvertently dereference values,
# same applies for const specifications)
#
# always add whitespace after type definition for cosmetic reasons
known_types = [
'const realtype *',
'const double *',
'const realtype ',
'double *',
'realtype *',
'const int ',
'int ',
'SUNMatrixContent_Sparse ',
'gsl::span<const int>'
]
for type_str in known_types:
signature = signature.replace(type_str, '')
return signature
[docs]def is_valid_identifier(x: str) -> bool:
"""
Check whether `x` is a valid identifier for conditions, parameters,
observables... . Identifiers may only contain upper and lower case letters,
digits and underscores, and must not start with a digit.
:param x:
string to check
:return:
``True`` if valid, ``False`` otherwise
"""
return IDENTIFIER_PATTERN.match(x) is not None
@contextlib.contextmanager
def _monkeypatched(obj: object, name: str, patch: Any):
"""
Temporarily monkeypatches an object.
:param obj:
object to be patched
:param name:
name of the attribute to be patched
:param patch:
patched value
"""
pre_patched_value = getattr(obj, name)
setattr(obj, name, patch)
try:
yield object
finally:
setattr(obj, name, pre_patched_value)
def _custom_pow_eval_derivative(self, s):
"""
Custom Pow derivative that removes a removable singularity for
``self.base == 0`` and ``self.base.diff(s) == 0``. This function is
intended to be monkeypatched into :py:method:`sympy.Pow._eval_derivative`.
:param self:
sp.Pow class
:param s:
variable with respect to which the derivative will be computed
"""
dbase = self.base.diff(s)
dexp = self.exp.diff(s)
part1 = sp.Pow(self.base, self.exp - 1) * self.exp * dbase
part2 = self * dexp * sp.log(self.base)
if self.base.is_nonzero or dbase.is_nonzero or part2.is_zero:
# first piece never applies or is zero anyways
return part1 + part2
return part1 + sp.Piecewise(
(self.base, sp.And(sp.Eq(self.base, 0), sp.Eq(dbase, 0))),
(part2, True)
)
def _jacobian_element(i, j, eq_i, sym_var_j):
"""Compute a single element of a jacobian"""
return (i, j), eq_i.diff(sym_var_j)
def _parallel_applyfunc(
obj: sp.Matrix,
func: Callable
) -> sp.Matrix:
"""Parallel implementation of sympy's Matrix.applyfunc"""
if (n_procs := int(os.environ.get("AMICI_IMPORT_NPROCS", 1))) == 1:
# serial
return obj.applyfunc(func)
# parallel
from pickle import PicklingError
from sympy.matrices.dense import DenseMatrix
from multiprocessing import get_context
# "spawn" should avoid potential deadlocks occurring with fork
# see e.g. https://stackoverflow.com/a/66113051
ctx = get_context('spawn')
with ctx.Pool(n_procs) as p:
try:
if isinstance(obj, DenseMatrix):
return obj._new(obj.rows, obj.cols, p.map(func, obj))
elif isinstance(obj, sp.SparseMatrix):
dok = obj.todok()
mapped = p.map(func, dok.values())
dok = {k: v for k, v in zip(dok.keys(), mapped) if v != 0}
return obj._new(obj.rows, obj.cols, dok)
else:
raise ValueError(f"Unsupported matrix type {type(obj)}")
except PicklingError as e:
raise ValueError(
f"Couldn't pickle {func}. This is likely because the argument "
"was not a module-level function. Either rewrite the argument "
"to a module-level function or disable parallelization by "
"setting `AMICI_IMPORT_NPROCS=1`."
) from e