663 lines
27 KiB
Python
663 lines
27 KiB
Python
import inspect
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import numpy as np
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from .bdf import BDF
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from .radau import Radau
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from .rk import RK23, RK45, DOP853
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from .lsoda import LSODA
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from scipy.optimize import OptimizeResult
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from .common import EPS, OdeSolution
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from .base import OdeSolver
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METHODS = {'RK23': RK23,
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'RK45': RK45,
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'DOP853': DOP853,
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'Radau': Radau,
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'BDF': BDF,
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'LSODA': LSODA}
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MESSAGES = {0: "The solver successfully reached the end of the integration interval.",
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1: "A termination event occurred."}
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class OdeResult(OptimizeResult):
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pass
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def prepare_events(events):
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"""Standardize event functions and extract is_terminal and direction."""
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if callable(events):
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events = (events,)
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if events is not None:
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is_terminal = np.empty(len(events), dtype=bool)
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direction = np.empty(len(events))
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for i, event in enumerate(events):
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try:
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is_terminal[i] = event.terminal
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except AttributeError:
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is_terminal[i] = False
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try:
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direction[i] = event.direction
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except AttributeError:
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direction[i] = 0
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else:
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is_terminal = None
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direction = None
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return events, is_terminal, direction
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def solve_event_equation(event, sol, t_old, t):
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"""Solve an equation corresponding to an ODE event.
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The equation is ``event(t, y(t)) = 0``, here ``y(t)`` is known from an
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ODE solver using some sort of interpolation. It is solved by
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`scipy.optimize.brentq` with xtol=atol=4*EPS.
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Parameters
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----------
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event : callable
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Function ``event(t, y)``.
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sol : callable
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Function ``sol(t)`` which evaluates an ODE solution between `t_old`
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and `t`.
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t_old, t : float
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Previous and new values of time. They will be used as a bracketing
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interval.
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Returns
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-------
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root : float
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Found solution.
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"""
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from scipy.optimize import brentq
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return brentq(lambda t: event(t, sol(t)), t_old, t,
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xtol=4 * EPS, rtol=4 * EPS)
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def handle_events(sol, events, active_events, is_terminal, t_old, t):
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"""Helper function to handle events.
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Parameters
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----------
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sol : DenseOutput
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Function ``sol(t)`` which evaluates an ODE solution between `t_old`
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and `t`.
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events : list of callables, length n_events
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Event functions with signatures ``event(t, y)``.
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active_events : ndarray
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Indices of events which occurred.
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is_terminal : ndarray, shape (n_events,)
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Which events are terminal.
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t_old, t : float
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Previous and new values of time.
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Returns
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-------
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root_indices : ndarray
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Indices of events which take zero between `t_old` and `t` and before
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a possible termination.
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roots : ndarray
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Values of t at which events occurred.
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terminate : bool
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Whether a terminal event occurred.
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"""
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roots = [solve_event_equation(events[event_index], sol, t_old, t)
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for event_index in active_events]
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roots = np.asarray(roots)
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if np.any(is_terminal[active_events]):
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if t > t_old:
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order = np.argsort(roots)
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else:
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order = np.argsort(-roots)
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active_events = active_events[order]
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roots = roots[order]
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t = np.nonzero(is_terminal[active_events])[0][0]
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active_events = active_events[:t + 1]
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roots = roots[:t + 1]
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terminate = True
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else:
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terminate = False
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return active_events, roots, terminate
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def find_active_events(g, g_new, direction):
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"""Find which event occurred during an integration step.
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Parameters
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----------
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g, g_new : array_like, shape (n_events,)
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Values of event functions at a current and next points.
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direction : ndarray, shape (n_events,)
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Event "direction" according to the definition in `solve_ivp`.
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Returns
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-------
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active_events : ndarray
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Indices of events which occurred during the step.
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"""
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g, g_new = np.asarray(g), np.asarray(g_new)
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up = (g <= 0) & (g_new >= 0)
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down = (g >= 0) & (g_new <= 0)
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either = up | down
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mask = (up & (direction > 0) |
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down & (direction < 0) |
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either & (direction == 0))
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return np.nonzero(mask)[0]
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def solve_ivp(fun, t_span, y0, method='RK45', t_eval=None, dense_output=False,
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events=None, vectorized=False, args=None, **options):
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"""Solve an initial value problem for a system of ODEs.
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This function numerically integrates a system of ordinary differential
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equations given an initial value::
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dy / dt = f(t, y)
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y(t0) = y0
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Here t is a 1-D independent variable (time), y(t) is an
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N-D vector-valued function (state), and an N-D
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vector-valued function f(t, y) determines the differential equations.
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The goal is to find y(t) approximately satisfying the differential
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equations, given an initial value y(t0)=y0.
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Some of the solvers support integration in the complex domain, but note
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that for stiff ODE solvers, the right-hand side must be
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complex-differentiable (satisfy Cauchy-Riemann equations [11]_).
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To solve a problem in the complex domain, pass y0 with a complex data type.
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Another option always available is to rewrite your problem for real and
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imaginary parts separately.
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Parameters
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----------
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fun : callable
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Right-hand side of the system. The calling signature is ``fun(t, y)``.
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Here `t` is a scalar, and there are two options for the ndarray `y`:
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It can either have shape (n,); then `fun` must return array_like with
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shape (n,). Alternatively, it can have shape (n, k); then `fun`
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must return an array_like with shape (n, k), i.e., each column
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corresponds to a single column in `y`. The choice between the two
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options is determined by `vectorized` argument (see below). The
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vectorized implementation allows a faster approximation of the Jacobian
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by finite differences (required for stiff solvers).
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t_span : 2-tuple of floats
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Interval of integration (t0, tf). The solver starts with t=t0 and
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integrates until it reaches t=tf.
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y0 : array_like, shape (n,)
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Initial state. For problems in the complex domain, pass `y0` with a
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complex data type (even if the initial value is purely real).
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method : string or `OdeSolver`, optional
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Integration method to use:
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* 'RK45' (default): Explicit Runge-Kutta method of order 5(4) [1]_.
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The error is controlled assuming accuracy of the fourth-order
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method, but steps are taken using the fifth-order accurate
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formula (local extrapolation is done). A quartic interpolation
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polynomial is used for the dense output [2]_. Can be applied in
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the complex domain.
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* 'RK23': Explicit Runge-Kutta method of order 3(2) [3]_. The error
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is controlled assuming accuracy of the second-order method, but
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steps are taken using the third-order accurate formula (local
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extrapolation is done). A cubic Hermite polynomial is used for the
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dense output. Can be applied in the complex domain.
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* 'DOP853': Explicit Runge-Kutta method of order 8 [13]_.
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Python implementation of the "DOP853" algorithm originally
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written in Fortran [14]_. A 7-th order interpolation polynomial
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accurate to 7-th order is used for the dense output.
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Can be applied in the complex domain.
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* 'Radau': Implicit Runge-Kutta method of the Radau IIA family of
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order 5 [4]_. The error is controlled with a third-order accurate
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embedded formula. A cubic polynomial which satisfies the
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collocation conditions is used for the dense output.
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* 'BDF': Implicit multi-step variable-order (1 to 5) method based
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on a backward differentiation formula for the derivative
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approximation [5]_. The implementation follows the one described
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in [6]_. A quasi-constant step scheme is used and accuracy is
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enhanced using the NDF modification. Can be applied in the
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complex domain.
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* 'LSODA': Adams/BDF method with automatic stiffness detection and
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switching [7]_, [8]_. This is a wrapper of the Fortran solver
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from ODEPACK.
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Explicit Runge-Kutta methods ('RK23', 'RK45', 'DOP853') should be used
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for non-stiff problems and implicit methods ('Radau', 'BDF') for
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stiff problems [9]_. Among Runge-Kutta methods, 'DOP853' is recommended
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for solving with high precision (low values of `rtol` and `atol`).
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If not sure, first try to run 'RK45'. If it makes unusually many
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iterations, diverges, or fails, your problem is likely to be stiff and
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you should use 'Radau' or 'BDF'. 'LSODA' can also be a good universal
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choice, but it might be somewhat less convenient to work with as it
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wraps old Fortran code.
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You can also pass an arbitrary class derived from `OdeSolver` which
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implements the solver.
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t_eval : array_like or None, optional
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Times at which to store the computed solution, must be sorted and lie
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within `t_span`. If None (default), use points selected by the solver.
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dense_output : bool, optional
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Whether to compute a continuous solution. Default is False.
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events : callable, or list of callables, optional
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Events to track. If None (default), no events will be tracked.
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Each event occurs at the zeros of a continuous function of time and
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state. Each function must have the signature ``event(t, y)`` and return
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a float. The solver will find an accurate value of `t` at which
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``event(t, y(t)) = 0`` using a root-finding algorithm. By default, all
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zeros will be found. The solver looks for a sign change over each step,
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so if multiple zero crossings occur within one step, events may be
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missed. Additionally each `event` function might have the following
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attributes:
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terminal: bool, optional
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Whether to terminate integration if this event occurs.
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Implicitly False if not assigned.
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direction: float, optional
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Direction of a zero crossing. If `direction` is positive,
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`event` will only trigger when going from negative to positive,
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and vice versa if `direction` is negative. If 0, then either
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direction will trigger event. Implicitly 0 if not assigned.
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You can assign attributes like ``event.terminal = True`` to any
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function in Python.
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vectorized : bool, optional
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Whether `fun` is implemented in a vectorized fashion. Default is False.
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args : tuple, optional
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Additional arguments to pass to the user-defined functions. If given,
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the additional arguments are passed to all user-defined functions.
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So if, for example, `fun` has the signature ``fun(t, y, a, b, c)``,
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then `jac` (if given) and any event functions must have the same
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signature, and `args` must be a tuple of length 3.
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options
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Options passed to a chosen solver. All options available for already
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implemented solvers are listed below.
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first_step : float or None, optional
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Initial step size. Default is `None` which means that the algorithm
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should choose.
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max_step : float, optional
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Maximum allowed step size. Default is np.inf, i.e., the step size is not
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bounded and determined solely by the solver.
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rtol, atol : float or array_like, optional
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Relative and absolute tolerances. The solver keeps the local error
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estimates less than ``atol + rtol * abs(y)``. Here `rtol` controls a
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relative accuracy (number of correct digits). But if a component of `y`
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is approximately below `atol`, the error only needs to fall within
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the same `atol` threshold, and the number of correct digits is not
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guaranteed. If components of y have different scales, it might be
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beneficial to set different `atol` values for different components by
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passing array_like with shape (n,) for `atol`. Default values are
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1e-3 for `rtol` and 1e-6 for `atol`.
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jac : array_like, sparse_matrix, callable or None, optional
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Jacobian matrix of the right-hand side of the system with respect
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to y, required by the 'Radau', 'BDF' and 'LSODA' method. The
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Jacobian matrix has shape (n, n) and its element (i, j) is equal to
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``d f_i / d y_j``. There are three ways to define the Jacobian:
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* If array_like or sparse_matrix, the Jacobian is assumed to
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be constant. Not supported by 'LSODA'.
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* If callable, the Jacobian is assumed to depend on both
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t and y; it will be called as ``jac(t, y)``, as necessary.
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For 'Radau' and 'BDF' methods, the return value might be a
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sparse matrix.
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* If None (default), the Jacobian will be approximated by
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finite differences.
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It is generally recommended to provide the Jacobian rather than
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relying on a finite-difference approximation.
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jac_sparsity : array_like, sparse matrix or None, optional
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Defines a sparsity structure of the Jacobian matrix for a finite-
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difference approximation. Its shape must be (n, n). This argument
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is ignored if `jac` is not `None`. If the Jacobian has only few
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non-zero elements in *each* row, providing the sparsity structure
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will greatly speed up the computations [10]_. A zero entry means that
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a corresponding element in the Jacobian is always zero. If None
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(default), the Jacobian is assumed to be dense.
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Not supported by 'LSODA', see `lband` and `uband` instead.
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lband, uband : int or None, optional
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Parameters defining the bandwidth of the Jacobian for the 'LSODA'
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method, i.e., ``jac[i, j] != 0 only for i - lband <= j <= i + uband``.
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Default is None. Setting these requires your jac routine to return the
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Jacobian in the packed format: the returned array must have ``n``
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columns and ``uband + lband + 1`` rows in which Jacobian diagonals are
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written. Specifically ``jac_packed[uband + i - j , j] = jac[i, j]``.
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The same format is used in `scipy.linalg.solve_banded` (check for an
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illustration). These parameters can be also used with ``jac=None`` to
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reduce the number of Jacobian elements estimated by finite differences.
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min_step : float, optional
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The minimum allowed step size for 'LSODA' method.
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By default `min_step` is zero.
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Returns
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-------
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Bunch object with the following fields defined:
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t : ndarray, shape (n_points,)
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Time points.
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y : ndarray, shape (n, n_points)
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Values of the solution at `t`.
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sol : `OdeSolution` or None
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Found solution as `OdeSolution` instance; None if `dense_output` was
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set to False.
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t_events : list of ndarray or None
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Contains for each event type a list of arrays at which an event of
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that type event was detected. None if `events` was None.
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y_events : list of ndarray or None
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For each value of `t_events`, the corresponding value of the solution.
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None if `events` was None.
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nfev : int
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Number of evaluations of the right-hand side.
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njev : int
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Number of evaluations of the Jacobian.
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nlu : int
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Number of LU decompositions.
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status : int
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Reason for algorithm termination:
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* -1: Integration step failed.
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* 0: The solver successfully reached the end of `tspan`.
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* 1: A termination event occurred.
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message : string
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Human-readable description of the termination reason.
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success : bool
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True if the solver reached the interval end or a termination event
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occurred (``status >= 0``).
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References
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----------
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.. [1] J. R. Dormand, P. J. Prince, "A family of embedded Runge-Kutta
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formulae", Journal of Computational and Applied Mathematics, Vol. 6,
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No. 1, pp. 19-26, 1980.
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.. [2] L. W. Shampine, "Some Practical Runge-Kutta Formulas", Mathematics
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of Computation,, Vol. 46, No. 173, pp. 135-150, 1986.
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.. [3] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas",
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Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989.
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.. [4] E. Hairer, G. Wanner, "Solving Ordinary Differential Equations II:
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Stiff and Differential-Algebraic Problems", Sec. IV.8.
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.. [5] `Backward Differentiation Formula
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<https://en.wikipedia.org/wiki/Backward_differentiation_formula>`_
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on Wikipedia.
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.. [6] L. F. Shampine, M. W. Reichelt, "THE MATLAB ODE SUITE", SIAM J. SCI.
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COMPUTE., Vol. 18, No. 1, pp. 1-22, January 1997.
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.. [7] A. C. Hindmarsh, "ODEPACK, A Systematized Collection of ODE
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Solvers," IMACS Transactions on Scientific Computation, Vol 1.,
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pp. 55-64, 1983.
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.. [8] L. Petzold, "Automatic selection of methods for solving stiff and
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nonstiff systems of ordinary differential equations", SIAM Journal
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on Scientific and Statistical Computing, Vol. 4, No. 1, pp. 136-148,
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1983.
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.. [9] `Stiff equation <https://en.wikipedia.org/wiki/Stiff_equation>`_ on
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Wikipedia.
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.. [10] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of
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sparse Jacobian matrices", Journal of the Institute of Mathematics
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and its Applications, 13, pp. 117-120, 1974.
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.. [11] `Cauchy-Riemann equations
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<https://en.wikipedia.org/wiki/Cauchy-Riemann_equations>`_ on
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Wikipedia.
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.. [12] `Lotka-Volterra equations
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<https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations>`_
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on Wikipedia.
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.. [13] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential
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Equations I: Nonstiff Problems", Sec. II.
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.. [14] `Page with original Fortran code of DOP853
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<http://www.unige.ch/~hairer/software.html>`_.
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Examples
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--------
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Basic exponential decay showing automatically chosen time points.
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>>> from scipy.integrate import solve_ivp
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>>> def exponential_decay(t, y): return -0.5 * y
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>>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8])
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>>> print(sol.t)
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[ 0. 0.11487653 1.26364188 3.06061781 4.81611105 6.57445806
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8.33328988 10. ]
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>>> print(sol.y)
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[[2. 1.88836035 1.06327177 0.43319312 0.18017253 0.07483045
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0.03107158 0.01350781]
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[4. 3.7767207 2.12654355 0.86638624 0.36034507 0.14966091
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0.06214316 0.02701561]
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[8. 7.5534414 4.25308709 1.73277247 0.72069014 0.29932181
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0.12428631 0.05403123]]
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Specifying points where the solution is desired.
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>>> sol = solve_ivp(exponential_decay, [0, 10], [2, 4, 8],
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... t_eval=[0, 1, 2, 4, 10])
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>>> print(sol.t)
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[ 0 1 2 4 10]
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>>> print(sol.y)
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[[2. 1.21305369 0.73534021 0.27066736 0.01350938]
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[4. 2.42610739 1.47068043 0.54133472 0.02701876]
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[8. 4.85221478 2.94136085 1.08266944 0.05403753]]
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Cannon fired upward with terminal event upon impact. The ``terminal`` and
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``direction`` fields of an event are applied by monkey patching a function.
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Here ``y[0]`` is position and ``y[1]`` is velocity. The projectile starts
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at position 0 with velocity +10. Note that the integration never reaches
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t=100 because the event is terminal.
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>>> def upward_cannon(t, y): return [y[1], -0.5]
|
|
>>> def hit_ground(t, y): return y[0]
|
|
>>> 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([40.])]
|
|
>>> print(sol.t)
|
|
[0.00000000e+00 9.99900010e-05 1.09989001e-03 1.10988901e-02
|
|
1.11088891e-01 1.11098890e+00 1.11099890e+01 4.00000000e+01]
|
|
|
|
Use `dense_output` and `events` to find position, which is 100, at the apex
|
|
of the cannonball's trajectory. Apex is not defined as terminal, so both
|
|
apex and hit_ground are found. There is no information at t=20, so the sol
|
|
attribute is used to evaluate the solution. The sol attribute is returned
|
|
by setting ``dense_output=True``. Alternatively, the `y_events` attribute
|
|
can be used to access the solution at the time of the event.
|
|
|
|
>>> def apex(t, y): return y[1]
|
|
>>> sol = solve_ivp(upward_cannon, [0, 100], [0, 10],
|
|
... events=(hit_ground, apex), dense_output=True)
|
|
>>> print(sol.t_events)
|
|
[array([40.]), 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 4.00000000e+01]
|
|
>>> print(sol.sol(sol.t_events[1][0]))
|
|
[100. 0.]
|
|
>>> print(sol.y_events)
|
|
[array([[-5.68434189e-14, -1.00000000e+01]]), array([[1.00000000e+02, 1.77635684e-15]])]
|
|
|
|
As an example of a system with additional parameters, we'll implement
|
|
the Lotka-Volterra equations [12]_.
|
|
|
|
>>> def lotkavolterra(t, z, a, b, c, d):
|
|
... x, y = z
|
|
... return [a*x - b*x*y, -c*y + d*x*y]
|
|
...
|
|
|
|
We pass in the parameter values a=1.5, b=1, c=3 and d=1 with the `args`
|
|
argument.
|
|
|
|
>>> sol = solve_ivp(lotkavolterra, [0, 15], [10, 5], args=(1.5, 1, 3, 1),
|
|
... dense_output=True)
|
|
|
|
Compute a dense solution and plot it.
|
|
|
|
>>> t = np.linspace(0, 15, 300)
|
|
>>> z = sol.sol(t)
|
|
>>> import matplotlib.pyplot as plt
|
|
>>> plt.plot(t, z.T)
|
|
>>> plt.xlabel('t')
|
|
>>> plt.legend(['x', 'y'], shadow=True)
|
|
>>> plt.title('Lotka-Volterra System')
|
|
>>> plt.show()
|
|
|
|
"""
|
|
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 args is not None:
|
|
# Wrap the user's fun (and jac, if given) in lambdas to hide the
|
|
# additional parameters. Pass in the original fun as a keyword
|
|
# argument to keep it in the scope of the lambda.
|
|
fun = lambda t, x, fun=fun: fun(t, x, *args)
|
|
jac = options.get('jac')
|
|
if callable(jac):
|
|
options['jac'] = lambda t, x: jac(t, x, *args)
|
|
|
|
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]
|
|
elif t_eval is not None and dense_output:
|
|
ts = []
|
|
ti = [t0]
|
|
ys = []
|
|
else:
|
|
ts = []
|
|
ys = []
|
|
|
|
interpolants = []
|
|
|
|
events, is_terminal, event_dir = prepare_events(events)
|
|
|
|
if events is not None:
|
|
if args is not None:
|
|
# Wrap user functions in lambdas to hide the additional parameters.
|
|
# The original event function is passed as a keyword argument to the
|
|
# lambda to keep the original function in scope (i.e., avoid the
|
|
# late binding closure "gotcha").
|
|
events = [lambda t, x, event=event: event(t, x, *args)
|
|
for event in events]
|
|
g = [event(t0, y0) for event in events]
|
|
t_events = [[] for _ in range(len(events))]
|
|
y_events = [[] for _ in range(len(events))]
|
|
else:
|
|
t_events = None
|
|
y_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)
|
|
y_events[e].append(sol(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 0th 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
|
|
|
|
if t_eval is not None and dense_output:
|
|
ti.append(t)
|
|
|
|
message = MESSAGES.get(status, message)
|
|
|
|
if t_events is not None:
|
|
t_events = [np.asarray(te) for te in t_events]
|
|
y_events = [np.asarray(ye) for ye in y_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:
|
|
if t_eval is None:
|
|
sol = OdeSolution(ts, interpolants)
|
|
else:
|
|
sol = OdeSolution(ti, interpolants)
|
|
else:
|
|
sol = None
|
|
|
|
return OdeResult(t=ts, y=ys, sol=sol, t_events=t_events, y_events=y_events,
|
|
nfev=solver.nfev, njev=solver.njev, nlu=solver.nlu,
|
|
status=status, message=message, success=status >= 0)
|