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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/venv/lib/python3.9/site-packages/scipy/integrate/_ode.py

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Refactor on docs; Add performance comparison
4 years ago
# Authors: Pearu Peterson, Pauli Virtanen, John Travers
"""
First-order ODE integrators.
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
i = 0, ..., len(y0) - 1
class ode
---------
A generic interface class to numeric integrators. It has the following
methods::
integrator = ode(f, jac=None)
integrator = integrator.set_integrator(name, **params)
integrator = integrator.set_initial_value(y0, t0=0.0)
integrator = integrator.set_f_params(*args)
integrator = integrator.set_jac_params(*args)
y1 = integrator.integrate(t1, step=False, relax=False)
flag = integrator.successful()
class complex_ode
-----------------
This class has the same generic interface as ode, except it can handle complex
f, y and Jacobians by transparently translating them into the equivalent
real-valued system. It supports the real-valued solvers (i.e., not zvode) and is
an alternative to ode with the zvode solver, sometimes performing better.
"""
# XXX: Integrators must have:
# ===========================
# cvode - C version of vode and vodpk with many improvements.
# Get it from http://www.netlib.org/ode/cvode.tar.gz.
# To wrap cvode to Python, one must write the extension module by
# hand. Its interface is too much 'advanced C' that using f2py
# would be too complicated (or impossible).
#
# How to define a new integrator:
# ===============================
#
# class myodeint(IntegratorBase):
#
# runner = <odeint function> or None
#
# def __init__(self,...): # required
# <initialize>
#
# def reset(self,n,has_jac): # optional
# # n - the size of the problem (number of equations)
# # has_jac - whether user has supplied its own routine for Jacobian
# <allocate memory,initialize further>
#
# def run(self,f,jac,y0,t0,t1,f_params,jac_params): # required
# # this method is called to integrate from t=t0 to t=t1
# # with initial condition y0. f and jac are user-supplied functions
# # that define the problem. f_params,jac_params are additional
# # arguments
# # to these functions.
# <calculate y1>
# if <calculation was unsuccessful>:
# self.success = 0
# return t1,y1
#
# # In addition, one can define step() and run_relax() methods (they
# # take the same arguments as run()) if the integrator can support
# # these features (see IntegratorBase doc strings).
#
# if myodeint.runner:
# IntegratorBase.integrator_classes.append(myodeint)
__all__ = ['ode', 'complex_ode']
__version__ = "$Id$"
__docformat__ = "restructuredtext en"
import re
import warnings
from numpy import asarray, array, zeros, isscalar, real, imag, vstack
from . import vode as _vode
from . import _dop
from . import lsoda as _lsoda
_dop_int_dtype = _dop.types.intvar.dtype
_vode_int_dtype = _vode.types.intvar.dtype
_lsoda_int_dtype = _lsoda.types.intvar.dtype
# ------------------------------------------------------------------------------
# User interface
# ------------------------------------------------------------------------------
class ode(object):
"""
A generic interface class to numeric integrators.
Solve an equation system :math:`y'(t) = f(t,y)` with (optional) ``jac = df/dy``.
*Note*: The first two arguments of ``f(t, y, ...)`` are in the
opposite order of the arguments in the system definition function used
by `scipy.integrate.odeint`.
Parameters
----------
f : callable ``f(t, y, *f_args)``
Right-hand side of the differential equation. t is a scalar,
``y.shape == (n,)``.
``f_args`` is set by calling ``set_f_params(*args)``.
`f` should return a scalar, array or list (not a tuple).
jac : callable ``jac(t, y, *jac_args)``, optional
Jacobian of the right-hand side, ``jac[i,j] = d f[i] / d y[j]``.
``jac_args`` is set by calling ``set_jac_params(*args)``.
Attributes
----------
t : float
Current time.
y : ndarray
Current variable values.
See also
--------
odeint : an integrator with a simpler interface based on lsoda from ODEPACK
quad : for finding the area under a curve
Notes
-----
Available integrators are listed below. They can be selected using
the `set_integrator` method.
"vode"
Real-valued Variable-coefficient Ordinary Differential Equation
solver, with fixed-leading-coefficient implementation. It provides
implicit Adams method (for non-stiff problems) and a method based on
backward differentiation formulas (BDF) (for stiff problems).
Source: http://www.netlib.org/ode/vode.f
.. warning::
This integrator is not re-entrant. You cannot have two `ode`
instances using the "vode" integrator at the same time.
This integrator accepts the following parameters in `set_integrator`
method of the `ode` class:
- atol : float or sequence
absolute tolerance for solution
- rtol : float or sequence
relative tolerance for solution
- lband : None or int
- uband : None or int
Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+uband.
Setting these requires your jac routine to return the jacobian
in packed format, jac_packed[i-j+uband, j] = jac[i,j]. The
dimension of the matrix must be (lband+uband+1, len(y)).
- method: 'adams' or 'bdf'
Which solver to use, Adams (non-stiff) or BDF (stiff)
- with_jacobian : bool
This option is only considered when the user has not supplied a
Jacobian function and has not indicated (by setting either band)
that the Jacobian is banded. In this case, `with_jacobian` specifies
whether the iteration method of the ODE solver's correction step is
chord iteration with an internally generated full Jacobian or
functional iteration with no Jacobian.
- nsteps : int
Maximum number of (internally defined) steps allowed during one
call to the solver.
- first_step : float
- min_step : float
- max_step : float
Limits for the step sizes used by the integrator.
- order : int
Maximum order used by the integrator,
order <= 12 for Adams, <= 5 for BDF.
"zvode"
Complex-valued Variable-coefficient Ordinary Differential Equation
solver, with fixed-leading-coefficient implementation. It provides
implicit Adams method (for non-stiff problems) and a method based on
backward differentiation formulas (BDF) (for stiff problems).
Source: http://www.netlib.org/ode/zvode.f
.. warning::
This integrator is not re-entrant. You cannot have two `ode`
instances using the "zvode" integrator at the same time.
This integrator accepts the same parameters in `set_integrator`
as the "vode" solver.
.. note::
When using ZVODE for a stiff system, it should only be used for
the case in which the function f is analytic, that is, when each f(i)
is an analytic function of each y(j). Analyticity means that the
partial derivative df(i)/dy(j) is a unique complex number, and this
fact is critical in the way ZVODE solves the dense or banded linear
systems that arise in the stiff case. For a complex stiff ODE system
in which f is not analytic, ZVODE is likely to have convergence
failures, and for this problem one should instead use DVODE on the
equivalent real system (in the real and imaginary parts of y).
"lsoda"
Real-valued Variable-coefficient Ordinary Differential Equation
solver, with fixed-leading-coefficient implementation. It provides
automatic method switching between implicit Adams method (for non-stiff
problems) and a method based on backward differentiation formulas (BDF)
(for stiff problems).
Source: http://www.netlib.org/odepack
.. warning::
This integrator is not re-entrant. You cannot have two `ode`
instances using the "lsoda" integrator at the same time.
This integrator accepts the following parameters in `set_integrator`
method of the `ode` class:
- atol : float or sequence
absolute tolerance for solution
- rtol : float or sequence
relative tolerance for solution
- lband : None or int
- uband : None or int
Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+uband.
Setting these requires your jac routine to return the jacobian
in packed format, jac_packed[i-j+uband, j] = jac[i,j].
- with_jacobian : bool
*Not used.*
- nsteps : int
Maximum number of (internally defined) steps allowed during one
call to the solver.
- first_step : float
- min_step : float
- max_step : float
Limits for the step sizes used by the integrator.
- max_order_ns : int
Maximum order used in the nonstiff case (default 12).
- max_order_s : int
Maximum order used in the stiff case (default 5).
- max_hnil : int
Maximum number of messages reporting too small step size (t + h = t)
(default 0)
- ixpr : int
Whether to generate extra printing at method switches (default False).
"dopri5"
This is an explicit runge-kutta method of order (4)5 due to Dormand &
Prince (with stepsize control and dense output).
Authors:
E. Hairer and G. Wanner
Universite de Geneve, Dept. de Mathematiques
CH-1211 Geneve 24, Switzerland
e-mail: ernst.hairer@math.unige.ch, gerhard.wanner@math.unige.ch
This code is described in [HNW93]_.
This integrator accepts the following parameters in set_integrator()
method of the ode class:
- atol : float or sequence
absolute tolerance for solution
- rtol : float or sequence
relative tolerance for solution
- nsteps : int
Maximum number of (internally defined) steps allowed during one
call to the solver.
- first_step : float
- max_step : float
- safety : float
Safety factor on new step selection (default 0.9)
- ifactor : float
- dfactor : float
Maximum factor to increase/decrease step size by in one step
- beta : float
Beta parameter for stabilised step size control.
- verbosity : int
Switch for printing messages (< 0 for no messages).
"dop853"
This is an explicit runge-kutta method of order 8(5,3) due to Dormand
& Prince (with stepsize control and dense output).
Options and references the same as "dopri5".
Examples
--------
A problem to integrate and the corresponding jacobian:
>>> from scipy.integrate import ode
>>>
>>> y0, t0 = [1.0j, 2.0], 0
>>>
>>> def f(t, y, arg1):
... return [1j*arg1*y[0] + y[1], -arg1*y[1]**2]
>>> def jac(t, y, arg1):
... return [[1j*arg1, 1], [0, -arg1*2*y[1]]]
The integration:
>>> r = ode(f, jac).set_integrator('zvode', method='bdf')
>>> r.set_initial_value(y0, t0).set_f_params(2.0).set_jac_params(2.0)
>>> t1 = 10
>>> dt = 1
>>> while r.successful() and r.t < t1:
... print(r.t+dt, r.integrate(r.t+dt))
1 [-0.71038232+0.23749653j 0.40000271+0.j ]
2.0 [0.19098503-0.52359246j 0.22222356+0.j ]
3.0 [0.47153208+0.52701229j 0.15384681+0.j ]
4.0 [-0.61905937+0.30726255j 0.11764744+0.j ]
5.0 [0.02340997-0.61418799j 0.09523835+0.j ]
6.0 [0.58643071+0.339819j 0.08000018+0.j ]
7.0 [-0.52070105+0.44525141j 0.06896565+0.j ]
8.0 [-0.15986733-0.61234476j 0.06060616+0.j ]
9.0 [0.64850462+0.15048982j 0.05405414+0.j ]
10.0 [-0.38404699+0.56382299j 0.04878055+0.j ]
References
----------
.. [HNW93] E. Hairer, S.P. Norsett and G. Wanner, Solving Ordinary
Differential Equations i. Nonstiff Problems. 2nd edition.
Springer Series in Computational Mathematics,
Springer-Verlag (1993)
"""
def __init__(self, f, jac=None):
self.stiff = 0
self.f = f
self.jac = jac
self.f_params = ()
self.jac_params = ()
self._y = []
@property
def y(self):
return self._y
def set_initial_value(self, y, t=0.0):
"""Set initial conditions y(t) = y."""
if isscalar(y):
y = [y]
n_prev = len(self._y)
if not n_prev:
self.set_integrator('') # find first available integrator
self._y = asarray(y, self._integrator.scalar)
self.t = t
self._integrator.reset(len(self._y), self.jac is not None)
return self
def set_integrator(self, name, **integrator_params):
"""
Set integrator by name.
Parameters
----------
name : str
Name of the integrator.
integrator_params
Additional parameters for the integrator.
"""
integrator = find_integrator(name)
if integrator is None:
# FIXME: this really should be raise an exception. Will that break
# any code?
warnings.warn('No integrator name match with %r or is not '
'available.' % name)
else:
self._integrator = integrator(**integrator_params)
if not len(self._y):
self.t = 0.0
self._y = array([0.0], self._integrator.scalar)
self._integrator.reset(len(self._y), self.jac is not None)
return self
def integrate(self, t, step=False, relax=False):
"""Find y=y(t), set y as an initial condition, and return y.
Parameters
----------
t : float
The endpoint of the integration step.
step : bool
If True, and if the integrator supports the step method,
then perform a single integration step and return.
This parameter is provided in order to expose internals of
the implementation, and should not be changed from its default
value in most cases.
relax : bool
If True and if the integrator supports the run_relax method,
then integrate until t_1 >= t and return. ``relax`` is not
referenced if ``step=True``.
This parameter is provided in order to expose internals of
the implementation, and should not be changed from its default
value in most cases.
Returns
-------
y : float
The integrated value at t
"""
if step and self._integrator.supports_step:
mth = self._integrator.step
elif relax and self._integrator.supports_run_relax:
mth = self._integrator.run_relax
else:
mth = self._integrator.run
try:
self._y, self.t = mth(self.f, self.jac or (lambda: None),
self._y, self.t, t,
self.f_params, self.jac_params)
except SystemError:
# f2py issue with tuple returns, see ticket 1187.
raise ValueError('Function to integrate must not return a tuple.')
return self._y
def successful(self):
"""Check if integration was successful."""
try:
self._integrator
except AttributeError:
self.set_integrator('')
return self._integrator.success == 1
def get_return_code(self):
"""Extracts the return code for the integration to enable better control
if the integration fails.
In general, a return code > 0 implies success, while a return code < 0
implies failure.
Notes
-----
This section describes possible return codes and their meaning, for available
integrators that can be selected by `set_integrator` method.
"vode"
=========== =======
Return Code Message
=========== =======
2 Integration successful.
-1 Excess work done on this call. (Perhaps wrong MF.)
-2 Excess accuracy requested. (Tolerances too small.)
-3 Illegal input detected. (See printed message.)
-4 Repeated error test failures. (Check all input.)
-5 Repeated convergence failures. (Perhaps bad Jacobian
supplied or wrong choice of MF or tolerances.)
-6 Error weight became zero during problem. (Solution
component i vanished, and ATOL or ATOL(i) = 0.)
=========== =======
"zvode"
=========== =======
Return Code Message
=========== =======
2 Integration successful.
-1 Excess work done on this call. (Perhaps wrong MF.)
-2 Excess accuracy requested. (Tolerances too small.)
-3 Illegal input detected. (See printed message.)
-4 Repeated error test failures. (Check all input.)
-5 Repeated convergence failures. (Perhaps bad Jacobian
supplied or wrong choice of MF or tolerances.)
-6 Error weight became zero during problem. (Solution
component i vanished, and ATOL or ATOL(i) = 0.)
=========== =======
"dopri5"
=========== =======
Return Code Message
=========== =======
1 Integration successful.
2 Integration successful (interrupted by solout).
-1 Input is not consistent.
-2 Larger nsteps is needed.
-3 Step size becomes too small.
-4 Problem is probably stiff (interrupted).
=========== =======
"dop853"
=========== =======
Return Code Message
=========== =======
1 Integration successful.
2 Integration successful (interrupted by solout).
-1 Input is not consistent.
-2 Larger nsteps is needed.
-3 Step size becomes too small.
-4 Problem is probably stiff (interrupted).
=========== =======
"lsoda"
=========== =======
Return Code Message
=========== =======
2 Integration successful.
-1 Excess work done on this call (perhaps wrong Dfun type).
-2 Excess accuracy requested (tolerances too small).
-3 Illegal input detected (internal error).
-4 Repeated error test failures (internal error).
-5 Repeated convergence failures (perhaps bad Jacobian or tolerances).
-6 Error weight became zero during problem.
-7 Internal workspace insufficient to finish (internal error).
=========== =======
"""
try:
self._integrator
except AttributeError:
self.set_integrator('')
return self._integrator.istate
def set_f_params(self, *args):
"""Set extra parameters for user-supplied function f."""
self.f_params = args
return self
def set_jac_params(self, *args):
"""Set extra parameters for user-supplied function jac."""
self.jac_params = args
return self
def set_solout(self, solout):
"""
Set callable to be called at every successful integration step.
Parameters
----------
solout : callable
``solout(t, y)`` is called at each internal integrator step,
t is a scalar providing the current independent position
y is the current soloution ``y.shape == (n,)``
solout should return -1 to stop integration
otherwise it should return None or 0
"""
if self._integrator.supports_solout:
self._integrator.set_solout(solout)
if self._y is not None:
self._integrator.reset(len(self._y), self.jac is not None)
else:
raise ValueError("selected integrator does not support solout,"
" choose another one")
def _transform_banded_jac(bjac):
"""
Convert a real matrix of the form (for example)
[0 0 A B] [0 0 0 B]
[0 0 C D] [0 0 A D]
[E F G H] to [0 F C H]
[I J K L] [E J G L]
[I 0 K 0]
That is, every other column is shifted up one.
"""
# Shift every other column.
newjac = zeros((bjac.shape[0] + 1, bjac.shape[1]))
newjac[1:, ::2] = bjac[:, ::2]
newjac[:-1, 1::2] = bjac[:, 1::2]
return newjac
class complex_ode(ode):
"""
A wrapper of ode for complex systems.
This functions similarly as `ode`, but re-maps a complex-valued
equation system to a real-valued one before using the integrators.
Parameters
----------
f : callable ``f(t, y, *f_args)``
Rhs of the equation. t is a scalar, ``y.shape == (n,)``.
``f_args`` is set by calling ``set_f_params(*args)``.
jac : callable ``jac(t, y, *jac_args)``
Jacobian of the rhs, ``jac[i,j] = d f[i] / d y[j]``.
``jac_args`` is set by calling ``set_f_params(*args)``.
Attributes
----------
t : float
Current time.
y : ndarray
Current variable values.
Examples
--------
For usage examples, see `ode`.
"""
def __init__(self, f, jac=None):
self.cf = f
self.cjac = jac
if jac is None:
ode.__init__(self, self._wrap, None)
else:
ode.__init__(self, self._wrap, self._wrap_jac)
def _wrap(self, t, y, *f_args):
f = self.cf(*((t, y[::2] + 1j * y[1::2]) + f_args))
# self.tmp is a real-valued array containing the interleaved
# real and imaginary parts of f.
self.tmp[::2] = real(f)
self.tmp[1::2] = imag(f)
return self.tmp
def _wrap_jac(self, t, y, *jac_args):
# jac is the complex Jacobian computed by the user-defined function.
jac = self.cjac(*((t, y[::2] + 1j * y[1::2]) + jac_args))
# jac_tmp is the real version of the complex Jacobian. Each complex
# entry in jac, say 2+3j, becomes a 2x2 block of the form
# [2 -3]
# [3 2]
jac_tmp = zeros((2 * jac.shape[0], 2 * jac.shape[1]))
jac_tmp[1::2, 1::2] = jac_tmp[::2, ::2] = real(jac)
jac_tmp[1::2, ::2] = imag(jac)
jac_tmp[::2, 1::2] = -jac_tmp[1::2, ::2]
ml = getattr(self._integrator, 'ml', None)
mu = getattr(self._integrator, 'mu', None)
if ml is not None or mu is not None:
# Jacobian is banded. The user's Jacobian function has computed
# the complex Jacobian in packed format. The corresponding
# real-valued version has every other column shifted up.
jac_tmp = _transform_banded_jac(jac_tmp)
return jac_tmp
@property
def y(self):
return self._y[::2] + 1j * self._y[1::2]
def set_integrator(self, name, **integrator_params):
"""
Set integrator by name.
Parameters
----------
name : str
Name of the integrator
integrator_params
Additional parameters for the integrator.
"""
if name == 'zvode':
raise ValueError("zvode must be used with ode, not complex_ode")
lband = integrator_params.get('lband')
uband = integrator_params.get('uband')
if lband is not None or uband is not None:
# The Jacobian is banded. Override the user-supplied bandwidths
# (which are for the complex Jacobian) with the bandwidths of
# the corresponding real-valued Jacobian wrapper of the complex
# Jacobian.
integrator_params['lband'] = 2 * (lband or 0) + 1
integrator_params['uband'] = 2 * (uband or 0) + 1
return ode.set_integrator(self, name, **integrator_params)
def set_initial_value(self, y, t=0.0):
"""Set initial conditions y(t) = y."""
y = asarray(y)
self.tmp = zeros(y.size * 2, 'float')
self.tmp[::2] = real(y)
self.tmp[1::2] = imag(y)
return ode.set_initial_value(self, self.tmp, t)
def integrate(self, t, step=False, relax=False):
"""Find y=y(t), set y as an initial condition, and return y.
Parameters
----------
t : float
The endpoint of the integration step.
step : bool
If True, and if the integrator supports the step method,
then perform a single integration step and return.
This parameter is provided in order to expose internals of
the implementation, and should not be changed from its default
value in most cases.
relax : bool
If True and if the integrator supports the run_relax method,
then integrate until t_1 >= t and return. ``relax`` is not
referenced if ``step=True``.
This parameter is provided in order to expose internals of
the implementation, and should not be changed from its default
value in most cases.
Returns
-------
y : float
The integrated value at t
"""
y = ode.integrate(self, t, step, relax)
return y[::2] + 1j * y[1::2]
def set_solout(self, solout):
"""
Set callable to be called at every successful integration step.
Parameters
----------
solout : callable
``solout(t, y)`` is called at each internal integrator step,
t is a scalar providing the current independent position
y is the current soloution ``y.shape == (n,)``
solout should return -1 to stop integration
otherwise it should return None or 0
"""
if self._integrator.supports_solout:
self._integrator.set_solout(solout, complex=True)
else:
raise TypeError("selected integrator does not support solouta,"
+ "choose another one")
# ------------------------------------------------------------------------------
# ODE integrators
# ------------------------------------------------------------------------------
def find_integrator(name):
for cl in IntegratorBase.integrator_classes:
if re.match(name, cl.__name__, re.I):
return cl
return None
class IntegratorConcurrencyError(RuntimeError):
"""
Failure due to concurrent usage of an integrator that can be used
only for a single problem at a time.
"""
def __init__(self, name):
msg = ("Integrator `%s` can be used to solve only a single problem "
"at a time. If you want to integrate multiple problems, "
"consider using a different integrator "
"(see `ode.set_integrator`)") % name
RuntimeError.__init__(self, msg)
class IntegratorBase(object):
runner = None # runner is None => integrator is not available
success = None # success==1 if integrator was called successfully
istate = None # istate > 0 means success, istate < 0 means failure
supports_run_relax = None
supports_step = None
supports_solout = False
integrator_classes = []
scalar = float
def acquire_new_handle(self):
# Some of the integrators have internal state (ancient
# Fortran...), and so only one instance can use them at a time.
# We keep track of this, and fail when concurrent usage is tried.
self.__class__.active_global_handle += 1
self.handle = self.__class__.active_global_handle
def check_handle(self):
if self.handle is not self.__class__.active_global_handle:
raise IntegratorConcurrencyError(self.__class__.__name__)
def reset(self, n, has_jac):
"""Prepare integrator for call: allocate memory, set flags, etc.
n - number of equations.
has_jac - if user has supplied function for evaluating Jacobian.
"""
def run(self, f, jac, y0, t0, t1, f_params, jac_params):
"""Integrate from t=t0 to t=t1 using y0 as an initial condition.
Return 2-tuple (y1,t1) where y1 is the result and t=t1
defines the stoppage coordinate of the result.
"""
raise NotImplementedError('all integrators must define '
'run(f, jac, t0, t1, y0, f_params, jac_params)')
def step(self, f, jac, y0, t0, t1, f_params, jac_params):
"""Make one integration step and return (y1,t1)."""
raise NotImplementedError('%s does not support step() method' %
self.__class__.__name__)
def run_relax(self, f, jac, y0, t0, t1, f_params, jac_params):
"""Integrate from t=t0 to t>=t1 and return (y1,t)."""
raise NotImplementedError('%s does not support run_relax() method' %
self.__class__.__name__)
# XXX: __str__ method for getting visual state of the integrator
def _vode_banded_jac_wrapper(jacfunc, ml, jac_params):
"""
Wrap a banded Jacobian function with a function that pads
the Jacobian with `ml` rows of zeros.
"""
def jac_wrapper(t, y):
jac = asarray(jacfunc(t, y, *jac_params))
padded_jac = vstack((jac, zeros((ml, jac.shape[1]))))
return padded_jac
return jac_wrapper
class vode(IntegratorBase):
runner = getattr(_vode, 'dvode', None)
messages = {-1: 'Excess work done on this call. (Perhaps wrong MF.)',
-2: 'Excess accuracy requested. (Tolerances too small.)',
-3: 'Illegal input detected. (See printed message.)',
-4: 'Repeated error test failures. (Check all input.)',
-5: 'Repeated convergence failures. (Perhaps bad'
' Jacobian supplied or wrong choice of MF or tolerances.)',
-6: 'Error weight became zero during problem. (Solution'
' component i vanished, and ATOL or ATOL(i) = 0.)'
}
supports_run_relax = 1
supports_step = 1
active_global_handle = 0
def __init__(self,
method='adams',
with_jacobian=False,
rtol=1e-6, atol=1e-12,
lband=None, uband=None,
order=12,
nsteps=500,
max_step=0.0, # corresponds to infinite
min_step=0.0,
first_step=0.0, # determined by solver
):
if re.match(method, r'adams', re.I):
self.meth = 1
elif re.match(method, r'bdf', re.I):
self.meth = 2
else:
raise ValueError('Unknown integration method %s' % method)
self.with_jacobian = with_jacobian
self.rtol = rtol
self.atol = atol
self.mu = uband
self.ml = lband
self.order = order
self.nsteps = nsteps
self.max_step = max_step
self.min_step = min_step
self.first_step = first_step
self.success = 1
self.initialized = False
def _determine_mf_and_set_bands(self, has_jac):
"""
Determine the `MF` parameter (Method Flag) for the Fortran subroutine `dvode`.
In the Fortran code, the legal values of `MF` are:
10, 11, 12, 13, 14, 15, 20, 21, 22, 23, 24, 25,
-11, -12, -14, -15, -21, -22, -24, -25
but this Python wrapper does not use negative values.
Returns
mf = 10*self.meth + miter
self.meth is the linear multistep method:
self.meth == 1: method="adams"
self.meth == 2: method="bdf"
miter is the correction iteration method:
miter == 0: Functional iteraton; no Jacobian involved.
miter == 1: Chord iteration with user-supplied full Jacobian.
miter == 2: Chord iteration with internally computed full Jacobian.
miter == 3: Chord iteration with internally computed diagonal Jacobian.
miter == 4: Chord iteration with user-supplied banded Jacobian.
miter == 5: Chord iteration with internally computed banded Jacobian.
Side effects: If either self.mu or self.ml is not None and the other is None,
then the one that is None is set to 0.
"""
jac_is_banded = self.mu is not None or self.ml is not None
if jac_is_banded:
if self.mu is None:
self.mu = 0
if self.ml is None:
self.ml = 0
# has_jac is True if the user provided a Jacobian function.
if has_jac:
if jac_is_banded:
miter = 4
else:
miter = 1
else:
if jac_is_banded:
if self.ml == self.mu == 0:
miter = 3 # Chord iteration with internal diagonal Jacobian.
else:
miter = 5 # Chord iteration with internal banded Jacobian.
else:
# self.with_jacobian is set by the user in the call to ode.set_integrator.
if self.with_jacobian:
miter = 2 # Chord iteration with internal full Jacobian.
else:
miter = 0 # Functional iteraton; no Jacobian involved.
mf = 10 * self.meth + miter
return mf
def reset(self, n, has_jac):
mf = self._determine_mf_and_set_bands(has_jac)
if mf == 10:
lrw = 20 + 16 * n
elif mf in [11, 12]:
lrw = 22 + 16 * n + 2 * n * n
elif mf == 13:
lrw = 22 + 17 * n
elif mf in [14, 15]:
lrw = 22 + 18 * n + (3 * self.ml + 2 * self.mu) * n
elif mf == 20:
lrw = 20 + 9 * n
elif mf in [21, 22]:
lrw = 22 + 9 * n + 2 * n * n
elif mf == 23:
lrw = 22 + 10 * n
elif mf in [24, 25]:
lrw = 22 + 11 * n + (3 * self.ml + 2 * self.mu) * n
else:
raise ValueError('Unexpected mf=%s' % mf)
if mf % 10 in [0, 3]:
liw = 30
else:
liw = 30 + n
rwork = zeros((lrw,), float)
rwork[4] = self.first_step
rwork[5] = self.max_step
rwork[6] = self.min_step
self.rwork = rwork
iwork = zeros((liw,), _vode_int_dtype)
if self.ml is not None:
iwork[0] = self.ml
if self.mu is not None:
iwork[1] = self.mu
iwork[4] = self.order
iwork[5] = self.nsteps
iwork[6] = 2 # mxhnil
self.iwork = iwork
self.call_args = [self.rtol, self.atol, 1, 1,
self.rwork, self.iwork, mf]
self.success = 1
self.initialized = False
def run(self, f, jac, y0, t0, t1, f_params, jac_params):
if self.initialized:
self.check_handle()
else:
self.initialized = True
self.acquire_new_handle()
if self.ml is not None and self.ml > 0:
# Banded Jacobian. Wrap the user-provided function with one
# that pads the Jacobian array with the extra `self.ml` rows
# required by the f2py-generated wrapper.
jac = _vode_banded_jac_wrapper(jac, self.ml, jac_params)
args = ((f, jac, y0, t0, t1) + tuple(self.call_args) +
(f_params, jac_params))
y1, t, istate = self.runner(*args)
self.istate = istate
if istate < 0:
unexpected_istate_msg = 'Unexpected istate={:d}'.format(istate)
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
self.messages.get(istate, unexpected_istate_msg)))
self.success = 0
else:
self.call_args[3] = 2 # upgrade istate from 1 to 2
self.istate = 2
return y1, t
def step(self, *args):
itask = self.call_args[2]
self.call_args[2] = 2
r = self.run(*args)
self.call_args[2] = itask
return r
def run_relax(self, *args):
itask = self.call_args[2]
self.call_args[2] = 3
r = self.run(*args)
self.call_args[2] = itask
return r
if vode.runner is not None:
IntegratorBase.integrator_classes.append(vode)
class zvode(vode):
runner = getattr(_vode, 'zvode', None)
supports_run_relax = 1
supports_step = 1
scalar = complex
active_global_handle = 0
def reset(self, n, has_jac):
mf = self._determine_mf_and_set_bands(has_jac)
if mf in (10,):
lzw = 15 * n
elif mf in (11, 12):
lzw = 15 * n + 2 * n ** 2
elif mf in (-11, -12):
lzw = 15 * n + n ** 2
elif mf in (13,):
lzw = 16 * n
elif mf in (14, 15):
lzw = 17 * n + (3 * self.ml + 2 * self.mu) * n
elif mf in (-14, -15):
lzw = 16 * n + (2 * self.ml + self.mu) * n
elif mf in (20,):
lzw = 8 * n
elif mf in (21, 22):
lzw = 8 * n + 2 * n ** 2
elif mf in (-21, -22):
lzw = 8 * n + n ** 2
elif mf in (23,):
lzw = 9 * n
elif mf in (24, 25):
lzw = 10 * n + (3 * self.ml + 2 * self.mu) * n
elif mf in (-24, -25):
lzw = 9 * n + (2 * self.ml + self.mu) * n
lrw = 20 + n
if mf % 10 in (0, 3):
liw = 30
else:
liw = 30 + n
zwork = zeros((lzw,), complex)
self.zwork = zwork
rwork = zeros((lrw,), float)
rwork[4] = self.first_step
rwork[5] = self.max_step
rwork[6] = self.min_step
self.rwork = rwork
iwork = zeros((liw,), _vode_int_dtype)
if self.ml is not None:
iwork[0] = self.ml
if self.mu is not None:
iwork[1] = self.mu
iwork[4] = self.order
iwork[5] = self.nsteps
iwork[6] = 2 # mxhnil
self.iwork = iwork
self.call_args = [self.rtol, self.atol, 1, 1,
self.zwork, self.rwork, self.iwork, mf]
self.success = 1
self.initialized = False
if zvode.runner is not None:
IntegratorBase.integrator_classes.append(zvode)
class dopri5(IntegratorBase):
runner = getattr(_dop, 'dopri5', None)
name = 'dopri5'
supports_solout = True
messages = {1: 'computation successful',
2: 'computation successful (interrupted by solout)',
-1: 'input is not consistent',
-2: 'larger nsteps is needed',
-3: 'step size becomes too small',
-4: 'problem is probably stiff (interrupted)',
}
def __init__(self,
rtol=1e-6, atol=1e-12,
nsteps=500,
max_step=0.0,
first_step=0.0, # determined by solver
safety=0.9,
ifactor=10.0,
dfactor=0.2,
beta=0.0,
method=None,
verbosity=-1, # no messages if negative
):
self.rtol = rtol
self.atol = atol
self.nsteps = nsteps
self.max_step = max_step
self.first_step = first_step
self.safety = safety
self.ifactor = ifactor
self.dfactor = dfactor
self.beta = beta
self.verbosity = verbosity
self.success = 1
self.set_solout(None)
def set_solout(self, solout, complex=False):
self.solout = solout
self.solout_cmplx = complex
if solout is None:
self.iout = 0
else:
self.iout = 1
def reset(self, n, has_jac):
work = zeros((8 * n + 21,), float)
work[1] = self.safety
work[2] = self.dfactor
work[3] = self.ifactor
work[4] = self.beta
work[5] = self.max_step
work[6] = self.first_step
self.work = work
iwork = zeros((21,), _dop_int_dtype)
iwork[0] = self.nsteps
iwork[2] = self.verbosity
self.iwork = iwork
self.call_args = [self.rtol, self.atol, self._solout,
self.iout, self.work, self.iwork]
self.success = 1
def run(self, f, jac, y0, t0, t1, f_params, jac_params):
x, y, iwork, istate = self.runner(*((f, t0, y0, t1) +
tuple(self.call_args) + (f_params,)))
self.istate = istate
if istate < 0:
unexpected_istate_msg = 'Unexpected istate={:d}'.format(istate)
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
self.messages.get(istate, unexpected_istate_msg)))
self.success = 0
return y, x
def _solout(self, nr, xold, x, y, nd, icomp, con):
if self.solout is not None:
if self.solout_cmplx:
y = y[::2] + 1j * y[1::2]
return self.solout(x, y)
else:
return 1
if dopri5.runner is not None:
IntegratorBase.integrator_classes.append(dopri5)
class dop853(dopri5):
runner = getattr(_dop, 'dop853', None)
name = 'dop853'
def __init__(self,
rtol=1e-6, atol=1e-12,
nsteps=500,
max_step=0.0,
first_step=0.0, # determined by solver
safety=0.9,
ifactor=6.0,
dfactor=0.3,
beta=0.0,
method=None,
verbosity=-1, # no messages if negative
):
super(self.__class__, self).__init__(rtol, atol, nsteps, max_step,
first_step, safety, ifactor,
dfactor, beta, method,
verbosity)
def reset(self, n, has_jac):
work = zeros((11 * n + 21,), float)
work[1] = self.safety
work[2] = self.dfactor
work[3] = self.ifactor
work[4] = self.beta
work[5] = self.max_step
work[6] = self.first_step
self.work = work
iwork = zeros((21,), _dop_int_dtype)
iwork[0] = self.nsteps
iwork[2] = self.verbosity
self.iwork = iwork
self.call_args = [self.rtol, self.atol, self._solout,
self.iout, self.work, self.iwork]
self.success = 1
if dop853.runner is not None:
IntegratorBase.integrator_classes.append(dop853)
class lsoda(IntegratorBase):
runner = getattr(_lsoda, 'lsoda', None)
active_global_handle = 0
messages = {
2: "Integration successful.",
-1: "Excess work done on this call (perhaps wrong Dfun type).",
-2: "Excess accuracy requested (tolerances too small).",
-3: "Illegal input detected (internal error).",
-4: "Repeated error test failures (internal error).",
-5: "Repeated convergence failures (perhaps bad Jacobian or tolerances).",
-6: "Error weight became zero during problem.",
-7: "Internal workspace insufficient to finish (internal error)."
}
def __init__(self,
with_jacobian=False,
rtol=1e-6, atol=1e-12,
lband=None, uband=None,
nsteps=500,
max_step=0.0, # corresponds to infinite
min_step=0.0,
first_step=0.0, # determined by solver
ixpr=0,
max_hnil=0,
max_order_ns=12,
max_order_s=5,
method=None
):
self.with_jacobian = with_jacobian
self.rtol = rtol
self.atol = atol
self.mu = uband
self.ml = lband
self.max_order_ns = max_order_ns
self.max_order_s = max_order_s
self.nsteps = nsteps
self.max_step = max_step
self.min_step = min_step
self.first_step = first_step
self.ixpr = ixpr
self.max_hnil = max_hnil
self.success = 1
self.initialized = False
def reset(self, n, has_jac):
# Calculate parameters for Fortran subroutine dvode.
if has_jac:
if self.mu is None and self.ml is None:
jt = 1
else:
if self.mu is None:
self.mu = 0
if self.ml is None:
self.ml = 0
jt = 4
else:
if self.mu is None and self.ml is None:
jt = 2
else:
if self.mu is None:
self.mu = 0
if self.ml is None:
self.ml = 0
jt = 5
lrn = 20 + (self.max_order_ns + 4) * n
if jt in [1, 2]:
lrs = 22 + (self.max_order_s + 4) * n + n * n
elif jt in [4, 5]:
lrs = 22 + (self.max_order_s + 5 + 2 * self.ml + self.mu) * n
else:
raise ValueError('Unexpected jt=%s' % jt)
lrw = max(lrn, lrs)
liw = 20 + n
rwork = zeros((lrw,), float)
rwork[4] = self.first_step
rwork[5] = self.max_step
rwork[6] = self.min_step
self.rwork = rwork
iwork = zeros((liw,), _lsoda_int_dtype)
if self.ml is not None:
iwork[0] = self.ml
if self.mu is not None:
iwork[1] = self.mu
iwork[4] = self.ixpr
iwork[5] = self.nsteps
iwork[6] = self.max_hnil
iwork[7] = self.max_order_ns
iwork[8] = self.max_order_s
self.iwork = iwork
self.call_args = [self.rtol, self.atol, 1, 1,
self.rwork, self.iwork, jt]
self.success = 1
self.initialized = False
def run(self, f, jac, y0, t0, t1, f_params, jac_params):
if self.initialized:
self.check_handle()
else:
self.initialized = True
self.acquire_new_handle()
args = [f, y0, t0, t1] + self.call_args[:-1] + \
[jac, self.call_args[-1], f_params, 0, jac_params]
y1, t, istate = self.runner(*args)
self.istate = istate
if istate < 0:
unexpected_istate_msg = 'Unexpected istate={:d}'.format(istate)
warnings.warn('{:s}: {:s}'.format(self.__class__.__name__,
self.messages.get(istate, unexpected_istate_msg)))
self.success = 0
else:
self.call_args[3] = 2 # upgrade istate from 1 to 2
self.istate = 2
return y1, t
def step(self, *args):
itask = self.call_args[2]
self.call_args[2] = 2
r = self.run(*args)
self.call_args[2] = itask
return r
def run_relax(self, *args):
itask = self.call_args[2]
self.call_args[2] = 3
r = self.run(*args)
self.call_args[2] = itask
return r
if lsoda.runner:
IntegratorBase.integrator_classes.append(lsoda)