LSODA#
- class scipy.integrate.LSODA(fun, t0, y0, t_bound, first_step=None, min_step=0.0, max_step=inf, rtol=0.001, atol=1e-06, jac=None, lband=None, uband=None, vectorized=False, **extraneous)[source]#
Adams/BDF method with automatic stiffness detection and switching.
This is a wrapper to the Fortran solver from ODEPACK [1]. It switches automatically between the nonstiff Adams method and the stiff BDF method. The method was originally detailed in [2].
- Parameters:
- funcallable
Right-hand side of the system: the time derivative of the state
y
at timet
. The calling signature isfun(t, y)
, wheret
is a scalar andy
is an ndarray withlen(y) = len(y0)
.fun
must return an array of the same shape asy
. See vectorized for more information.- t0float
Initial time.
- y0array_like, shape (n,)
Initial state.
- t_boundfloat
Boundary time - the integration won’t continue beyond it. It also determines the direction of the integration.
- first_stepfloat or None, optional
Initial step size. Default is
None
which means that the algorithm should choose.- min_stepfloat, optional
Minimum allowed step size. Default is 0.0, i.e., the step size is not bounded and determined solely by the solver.
- max_stepfloat, optional
Maximum allowed step size. Default is np.inf, i.e., the step size is not bounded and determined solely by the solver.
- rtol, atolfloat and array_like, optional
Relative and absolute tolerances. The solver keeps the local error estimates less than
atol + rtol * abs(y)
. Here rtol controls a relative accuracy (number of correct digits), while atol controls absolute accuracy (number of correct decimal places). To achieve the desired rtol, set atol to be smaller than the smallest value that can be expected fromrtol * abs(y)
so that rtol dominates the allowable error. If atol is larger thanrtol * abs(y)
the number of correct digits is not guaranteed. Conversely, to achieve the desired atol set rtol such thatrtol * abs(y)
is always smaller than atol. If components of y have different scales, it might be beneficial to set different atol values for different components by passing array_like with shape (n,) for atol. Default values are 1e-3 for rtol and 1e-6 for atol.- jacNone or callable, optional
Jacobian matrix of the right-hand side of the system with respect to
y
. The Jacobian matrix has shape (n, n) and its element (i, j) is equal tod f_i / d y_j
. The function will be called asjac(t, y)
. If None (default), the Jacobian will be approximated by finite differences. It is generally recommended to provide the Jacobian rather than relying on a finite-difference approximation.- lband, ubandint or None
Parameters defining the bandwidth of the Jacobian, i.e.,
jac[i, j] != 0 only for i - lband <= j <= i + uband
. Setting these requires your jac routine to return the Jacobian in the packed format: the returned array must haven
columns anduband + lband + 1
rows in which Jacobian diagonals are written. Specificallyjac_packed[uband + i - j , j] = jac[i, j]
. The same format is used inscipy.linalg.solve_banded
(check for an illustration). These parameters can be also used withjac=None
to reduce the number of Jacobian elements estimated by finite differences.- vectorizedbool, optional
Whether fun may be called in a vectorized fashion. False (default) is recommended for this solver.
If
vectorized
is False, fun will always be called withy
of shape(n,)
, wheren = len(y0)
.If
vectorized
is True, fun may be called withy
of shape(n, k)
, wherek
is an integer. In this case, fun must behave such thatfun(t, y)[:, i] == fun(t, y[:, i])
(i.e. each column of the returned array is the time derivative of the state corresponding with a column ofy
).Setting
vectorized=True
allows for faster finite difference approximation of the Jacobian by methods ‘Radau’ and ‘BDF’, but will result in slower execution for this solver.
- Attributes:
- nint
Number of equations.
- statusstring
Current status of the solver: ‘running’, ‘finished’ or ‘failed’.
- t_boundfloat
Boundary time.
- directionfloat
Integration direction: +1 or -1.
- tfloat
Current time.
- yndarray
Current state.
- t_oldfloat
Previous time. None if no steps were made yet.
- nfevint
Number of evaluations of the right-hand side.
- njevint
Number of evaluations of the Jacobian.
Methods
Compute a local interpolant over the last successful step.
step
()Perform one integration step.
References
[1]A. C. Hindmarsh, “ODEPACK, A Systematized Collection of ODE Solvers,” IMACS Transactions on Scientific Computation, Vol 1., pp. 55-64, 1983.
[2]L. Petzold, “Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations”, SIAM Journal on Scientific and Statistical Computing, Vol. 4, No. 1, pp. 136-148, 1983.