- 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)#
Adams/BDF method with automatic stiffness detection and switching.
Right-hand side of the system: the time derivative of the state
t. The calling signature is
fun(t, y), where
tis a scalar and
yis an ndarray with
len(y) = len(y0).
funmust return an array of the same shape as
y. See vectorized for more information.
- y0array_like, shape (n,)
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
Nonewhich 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 from
rtol * abs(y)so that rtol dominates the allowable error. If atol is larger than
rtol * abs(y)the number of correct digits is not guaranteed. Conversely, to achieve the desired atol set rtol such that
rtol * 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 to
d f_i / d y_j. The function will be called as
jac(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 have
uband + lband + 1rows in which Jacobian diagonals are written. Specifically
jac_packed[uband + i - j , j] = jac[i, j]. The same format is used in
scipy.linalg.solve_banded(check for an illustration). These parameters can be also used with
jac=Noneto 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.
vectorizedis False, fun will always be called with
n = len(y0).
vectorizedis True, fun may be called with
(n, k), where
kis an integer. In this case, fun must behave such that
fun(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 of
vectorized=Trueallows for faster finite difference approximation of the Jacobian by methods ‘Radau’ and ‘BDF’, but will result in slower execution for this solver.
A. C. Hindmarsh, “ODEPACK, A Systematized Collection of ODE Solvers,” IMACS Transactions on Scientific Computation, Vol 1., pp. 55-64, 1983.
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.
Number of equations.
Current status of the solver: ‘running’, ‘finished’ or ‘failed’.
Integration direction: +1 or -1.
Previous time. None if no steps were made yet.
Number of evaluations of the right-hand side.
Number of evaluations of the Jacobian.
Compute a local interpolant over the last successful step.
Perform one integration step.