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'RK45': RK45, 'Radau': Radau, 'BDF': BDF, 'LSODA': LSODA}
1: "A termination event occurred."}
"""Standardize event functions and extract is_terminal and direction.""" if callable(events): events = (events,)
if events is not None: is_terminal = np.empty(len(events), dtype=bool) direction = np.empty(len(events)) for i, event in enumerate(events): try: is_terminal[i] = event.terminal except AttributeError: is_terminal[i] = False
try: direction[i] = event.direction except AttributeError: direction[i] = 0 else: is_terminal = None direction = None
return events, is_terminal, direction
"""Solve an equation corresponding to an ODE event.
The equation is ``event(t, y(t)) = 0``, here ``y(t)`` is known from an ODE solver using some sort of interpolation. It is solved by `scipy.optimize.brentq` with xtol=atol=4*EPS.
Parameters ---------- event : callable Function ``event(t, y)``. sol : callable Function ``sol(t)`` which evaluates an ODE solution between `t_old` and `t`. t_old, t : float Previous and new values of time. They will be used as a bracketing interval.
Returns ------- root : float Found solution. """ from scipy.optimize import brentq return brentq(lambda t: event(t, sol(t)), t_old, t, xtol=4 * EPS, rtol=4 * EPS)
"""Helper function to handle events.
Parameters ---------- sol : DenseOutput Function ``sol(t)`` which evaluates an ODE solution between `t_old` and `t`. events : list of callables, length n_events Event functions with signatures ``event(t, y)``. active_events : ndarray Indices of events which occurred. is_terminal : ndarray, shape (n_events,) Which events are terminal. t_old, t : float Previous and new values of time.
Returns ------- root_indices : ndarray Indices of events which take zero between `t_old` and `t` and before a possible termination. roots : ndarray Values of t at which events occurred. terminate : bool Whether a terminal event occurred. """ roots = [] for event_index in active_events: roots.append(solve_event_equation(events[event_index], sol, t_old, t))
roots = np.asarray(roots)
if np.any(is_terminal[active_events]): if t > t_old: order = np.argsort(roots) else: order = np.argsort(-roots) active_events = active_events[order] roots = roots[order] t = np.nonzero(is_terminal[active_events])[0][0] active_events = active_events[:t + 1] roots = roots[:t + 1] terminate = True else: terminate = False
return active_events, roots, terminate
"""Find which event occurred during an integration step.
Parameters ---------- g, g_new : array_like, shape (n_events,) Values of event functions at a current and next points. direction : ndarray, shape (n_events,) Event "direction" according to the definition in `solve_ivp`.
Returns ------- active_events : ndarray Indices of events which occurred during the step. """ g, g_new = np.asarray(g), np.asarray(g_new) up = (g <= 0) & (g_new >= 0) down = (g >= 0) & (g_new <= 0) either = up | down mask = (up & (direction > 0) | down & (direction < 0) | either & (direction == 0))
return np.nonzero(mask)[0]
events=None, vectorized=False, **options): """Solve an initial value problem for a system of ODEs.
This function numerically integrates a system of ordinary differential equations given an initial value::
dy / dt = f(t, y) y(t0) = y0
Here t is a one-dimensional independent variable (time), y(t) is an n-dimensional vector-valued function (state), and an n-dimensional vector-valued function f(t, y) determines the differential equations. The goal is to find y(t) approximately satisfying the differential equations, given an initial value y(t0)=y0.
Some of the solvers support integration in the complex domain, but note that for stiff ODE solvers, the right-hand side must be complex-differentiable (satisfy Cauchy-Riemann equations [11]_). To solve a problem in the complex domain, pass y0 with a complex data type. Another option is always to rewrite your problem for real and imaginary parts separately.
Parameters ---------- fun : callable Right-hand side of the system. The calling signature is ``fun(t, y)``. Here ``t`` is a scalar, and there are two options for the ndarray ``y``: It can either have shape (n,); then ``fun`` must return array_like with shape (n,). Alternatively it can have shape (n, k); then ``fun`` must return an array_like with shape (n, k), i.e. each column corresponds to a single column in ``y``. The choice between the two options is determined by `vectorized` argument (see below). The vectorized implementation allows a faster approximation of the Jacobian by finite differences (required for stiff solvers). t_span : 2-tuple of floats Interval of integration (t0, tf). The solver starts with t=t0 and integrates until it reaches t=tf. y0 : array_like, shape (n,) Initial state. For problems in the complex domain, pass `y0` with a complex data type (even if the initial guess is purely real). method : string or `OdeSolver`, optional Integration method to use:
* 'RK45' (default): Explicit Runge-Kutta method of order 5(4) [1]_. The error is controlled assuming accuracy of the fourth-order method, but steps are taken using the fifth-order accurate formula (local extrapolation is done). A quartic interpolation polynomial is used for the dense output [2]_. Can be applied in the complex domain. * 'RK23': Explicit Runge-Kutta method of order 3(2) [3]_. The error is controlled assuming accuracy of the second-order method, but steps are taken using the third-order accurate formula (local extrapolation is done). A cubic Hermite polynomial is used for the dense output. Can be applied in the complex domain. * 'Radau': Implicit Runge-Kutta method of the Radau IIA family of order 5 [4]_. The error is controlled with a third-order accurate embedded formula. A cubic polynomial which satisfies the collocation conditions is used for the dense output. * 'BDF': Implicit multi-step variable-order (1 to 5) method based on a backward differentiation formula for the derivative approximation [5]_. The implementation follows the one described in [6]_. A quasi-constant step scheme is used and accuracy is enhanced using the NDF modification. Can be applied in the complex domain. * 'LSODA': Adams/BDF method with automatic stiffness detection and switching [7]_, [8]_. This is a wrapper of the Fortran solver from ODEPACK.
You should use the 'RK45' or 'RK23' method for non-stiff problems and 'Radau' or 'BDF' for stiff problems [9]_. If not sure, first try to run 'RK45'. If needs unusually many iterations, diverges, or fails, your problem is likely to be stiff and you should use 'Radau' or 'BDF'. 'LSODA' can also be a good universal choice, but it might be somewhat less convenient to work with as it wraps old Fortran code.
You can also pass an arbitrary class derived from `OdeSolver` which implements the solver. dense_output : bool, optional Whether to compute a continuous solution. Default is False. t_eval : array_like or None, optional Times at which to store the computed solution, must be sorted and lie within `t_span`. If None (default), use points selected by the solver. events : callable, list of callables or None, optional Types of events to track. Each is defined by a continuous function of time and state that becomes zero value in case of an event. Each function must have the signature ``event(t, y)`` and return a float. The solver will find an accurate value of ``t`` at which ``event(t, y(t)) = 0`` using a root-finding algorithm. Additionally each ``event`` function might have the following attributes:
* terminal: bool, whether to terminate integration if this event occurs. Implicitly False if not assigned. * direction: float, direction of a zero crossing. If `direction` is positive, `event` must go from negative to positive, and vice versa if `direction` is negative. If 0, then either direction will count. Implicitly 0 if not assigned.
You can assign attributes like ``event.terminal = True`` to any function in Python. If None (default), events won't be tracked. vectorized : bool, optional Whether `fun` is implemented in a vectorized fashion. Default is False. options Options passed to a chosen solver. All options available for already implemented solvers are listed below. max_step : float, optional Maximum allowed step size. Default is np.inf, i.e. the step size is not bounded and determined solely by the solver. rtol, atol : float 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). But if a component of `y` is approximately below `atol`, the error only needs to fall within the same `atol` threshold, and the number of correct digits is not guaranteed. 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`. jac : {None, array_like, sparse_matrix, callable}, optional Jacobian matrix of the right-hand side of the system with respect to y, required by the 'Radau', 'BDF' and 'LSODA' method. The Jacobian matrix has shape (n, n) and its element (i, j) is equal to ``d f_i / d y_j``. There are three ways to define the Jacobian:
* If array_like or sparse_matrix, the Jacobian is assumed to be constant. Not supported by 'LSODA'. * If callable, the Jacobian is assumed to depend on both t and y; it will be called as ``jac(t, y)`` as necessary. For the 'Radau' and 'BDF' methods, the return value might be a sparse matrix. * 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. jac_sparsity : {None, array_like, sparse matrix}, optional Defines a sparsity structure of the Jacobian matrix for a finite-difference approximation. Its shape must be (n, n). This argument is ignored if `jac` is not `None`. If the Jacobian has only few non-zero elements in *each* row, providing the sparsity structure will greatly speed up the computations [10]_. A zero entry means that a corresponding element in the Jacobian is always zero. If None (default), the Jacobian is assumed to be dense. Not supported by 'LSODA', see `lband` and `uband` instead. lband, uband : int or None Parameters defining the bandwidth of the Jacobian for the 'LSODA' method, 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 ``n`` columns and ``uband + lband + 1`` rows 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=None`` to reduce the number of Jacobian elements estimated by finite differences. min_step, first_step : float, optional The minimum allowed step size and the initial step size respectively for 'LSODA' method. By default `min_step` is zero and `first_step` is selected automatically.
Returns ------- Bunch object with the following fields defined: t : ndarray, shape (n_points,) Time points. y : ndarray, shape (n, n_points) Values of the solution at `t`. sol : `OdeSolution` or None Found solution as `OdeSolution` instance; None if `dense_output` was set to False. t_events : list of ndarray or None Contains for each event type a list of arrays at which an event of that type event was detected. None if `events` was None. nfev : int Number of evaluations of the right-hand side. njev : int Number of evaluations of the Jacobian. nlu : int Number of LU decompositions. status : int Reason for algorithm termination:
* -1: Integration step failed. * 0: The solver successfully reached the end of `tspan`. * 1: A termination event occurred.
message : string Human-readable description of the termination reason. success : bool True if the solver reached the interval end or a termination event occurred (``status >= 0``).
References ---------- .. [1] J. R. Dormand, P. J. Prince, "A family of embedded Runge-Kutta formulae", Journal of Computational and Applied Mathematics, Vol. 6, No. 1, pp. 19-26, 1980. .. [2] L. W. Shampine, "Some Practical Runge-Kutta Formulas", Mathematics of Computation,, Vol. 46, No. 173, pp. 135-150, 1986. .. [3] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas", Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989. .. [4] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems", Sec. IV.8. .. [5] `Backward Differentiation Formula <https://en.wikipedia.org/wiki/Backward_differentiation_formula>`_ on Wikipedia. .. [6] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI. COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997. .. [7] A. C. Hindmarsh, "ODEPACK, A Systematized Collection of ODE Solvers," IMACS Transactions on Scientific Computation, Vol 1., pp. 55-64, 1983. .. [8] 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. .. [9] `Stiff equation <https://en.wikipedia.org/wiki/Stiff_equation>`_ on Wikipedia. .. [10] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of sparse Jacobian matrices", Journal of the Institute of Mathematics and its Applications, 13, pp. 117-120, 1974. .. [11] `Cauchy-Riemann equations <https://en.wikipedia.org/wiki/Cauchy-Riemann_equations>`_ on Wikipedia.
Examples -------- Basic exponential decay showing automatically chosen time points.
>>> from scipy.integrate import solve_ivp >>> def exponential_decay(t, y): return -0.5 * y >>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8]) >>> print(sol.t) [ 0. 0.11487653 1.26364188 3.06061781 4.85759374 6.65456967 8.4515456 10. ] >>> print(sol.y) [[2. 1.88836035 1.06327177 0.43319312 0.17648948 0.0719045 0.02929499 0.01350938] [4. 3.7767207 2.12654355 0.86638624 0.35297895 0.143809 0.05858998 0.02701876] [8. 7.5534414 4.25308709 1.73277247 0.7059579 0.287618 0.11717996 0.05403753]]
Specifying points where the solution is desired.
>>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8], ... t_eval=[0, 1, 2, 4, 10]) >>> print(sol.t) [ 0 1 2 4 10] >>> print(sol.y) [[2. 1.21305369 0.73534021 0.27066736 0.01350938] [4. 2.42610739 1.47068043 0.54133472 0.02701876] [8. 4.85221478 2.94136085 1.08266944 0.05403753]]
Cannon fired upward with terminal event upon impact. The ``terminal`` and ``direction`` fields of an event are applied by monkey patching a function. Here ``y[0]`` is position and ``y[1]`` is velocity. The projectile starts at position 0 with velocity +10. Note that the integration never reaches t=100 because the event is terminal.
>>> def upward_cannon(t, y): return [y[1], -0.5] >>> def hit_ground(t, y): return y[1] >>> hit_ground.terminal = True >>> hit_ground.direction = -1 >>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10], events=hit_ground) >>> print(sol.t_events) [array([ 20.])] >>> print(sol.t) [0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02 1.11088891e-01 1.11098890e+00 1.11099890e+01 2.00000000e+01] """ if method not in METHODS and not ( inspect.isclass(method) and issubclass(method, OdeSolver)): raise ValueError("`method` must be one of {} or OdeSolver class." .format(METHODS))
t0, tf = float(t_span[0]), float(t_span[1])
if t_eval is not None: t_eval = np.asarray(t_eval) if t_eval.ndim != 1: raise ValueError("`t_eval` must be 1-dimensional.")
if np.any(t_eval < min(t0, tf)) or np.any(t_eval > max(t0, tf)): raise ValueError("Values in `t_eval` are not within `t_span`.")
d = np.diff(t_eval) if tf > t0 and np.any(d <= 0) or tf < t0 and np.any(d >= 0): raise ValueError("Values in `t_eval` are not properly sorted.")
if tf > t0: t_eval_i = 0 else: # Make order of t_eval decreasing to use np.searchsorted. t_eval = t_eval[::-1] # This will be an upper bound for slices. t_eval_i = t_eval.shape[0]
if method in METHODS: method = METHODS[method]
solver = method(fun, t0, y0, tf, vectorized=vectorized, **options)
if t_eval is None: ts = [t0] ys = [y0] else: ts = [] ys = []
interpolants = []
events, is_terminal, event_dir = prepare_events(events)
if events is not None: g = [event(t0, y0) for event in events] t_events = [[] for _ in range(len(events))] else: t_events = None
status = None while status is None: message = solver.step()
if solver.status == 'finished': status = 0 elif solver.status == 'failed': status = -1 break
t_old = solver.t_old t = solver.t y = solver.y
if dense_output: sol = solver.dense_output() interpolants.append(sol) else: sol = None
if events is not None: g_new = [event(t, y) for event in events] active_events = find_active_events(g, g_new, event_dir) if active_events.size > 0: if sol is None: sol = solver.dense_output()
root_indices, roots, terminate = handle_events( sol, events, active_events, is_terminal, t_old, t)
for e, te in zip(root_indices, roots): t_events[e].append(te)
if terminate: status = 1 t = roots[-1] y = sol(t)
g = g_new
if t_eval is None: ts.append(t) ys.append(y) else: # The value in t_eval equal to t will be included. if solver.direction > 0: t_eval_i_new = np.searchsorted(t_eval, t, side='right') t_eval_step = t_eval[t_eval_i:t_eval_i_new] else: t_eval_i_new = np.searchsorted(t_eval, t, side='left') # It has to be done with two slice operations, because # you can't slice to 0-th element inclusive using backward # slicing. t_eval_step = t_eval[t_eval_i_new:t_eval_i][::-1]
if t_eval_step.size > 0: if sol is None: sol = solver.dense_output() ts.append(t_eval_step) ys.append(sol(t_eval_step)) t_eval_i = t_eval_i_new
message = MESSAGES.get(status, message)
if t_events is not None: t_events = [np.asarray(te) for te in t_events]
if t_eval is None: ts = np.array(ts) ys = np.vstack(ys).T else: ts = np.hstack(ts) ys = np.hstack(ys)
if dense_output: sol = OdeSolution(ts, interpolants) else: sol = None
return OdeResult(t=ts, y=ys, sol=sol, t_events=t_events, nfev=solver.nfev, njev=solver.njev, nlu=solver.nlu, status=status, message=message, success=status >= 0) |