import numpy as np from .base import OdeSolver, DenseOutput from .common import (validate_max_step, validate_tol, select_initial_step, norm, warn_extraneous, validate_first_step) from . import dop853_coefficients # Multiply steps computed from asymptotic behaviour of errors by this. SAFETY = 0.9 MIN_FACTOR = 0.2 # Minimum allowed decrease in a step size. MAX_FACTOR = 10 # Maximum allowed increase in a step size. def rk_step(fun, t, y, f, h, A, B, C, K): """Perform a single Runge-Kutta step. This function computes a prediction of an explicit Runge-Kutta method and also estimates the error of a less accurate method. Notation for Butcher tableau is as in [1]_. Parameters ---------- fun : callable Right-hand side of the system. t : float Current time. y : ndarray, shape (n,) Current state. f : ndarray, shape (n,) Current value of the derivative, i.e., ``fun(x, y)``. h : float Step to use. A : ndarray, shape (n_stages, n_stages) Coefficients for combining previous RK stages to compute the next stage. For explicit methods the coefficients at and above the main diagonal are zeros. B : ndarray, shape (n_stages,) Coefficients for combining RK stages for computing the final prediction. C : ndarray, shape (n_stages,) Coefficients for incrementing time for consecutive RK stages. The value for the first stage is always zero. K : ndarray, shape (n_stages + 1, n) Storage array for putting RK stages here. Stages are stored in rows. The last row is a linear combination of the previous rows with coefficients Returns ------- y_new : ndarray, shape (n,) Solution at t + h computed with a higher accuracy. f_new : ndarray, shape (n,) Derivative ``fun(t + h, y_new)``. References ---------- .. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential Equations I: Nonstiff Problems", Sec. II.4. """ K[0] = f for s, (a, c) in enumerate(zip(A[1:], C[1:]), start=1): dy = np.dot(K[:s].T, a[:s]) * h K[s] = fun(t + c * h, y + dy) y_new = y + h * np.dot(K[:-1].T, B) f_new = fun(t + h, y_new) K[-1] = f_new return y_new, f_new class RungeKutta(OdeSolver): """Base class for explicit Runge-Kutta methods.""" C = NotImplemented A = NotImplemented B = NotImplemented E = NotImplemented P = NotImplemented order = NotImplemented error_estimator_order = NotImplemented n_stages = NotImplemented def __init__(self, fun, t0, y0, t_bound, max_step=np.inf, rtol=1e-3, atol=1e-6, vectorized=False, first_step=None, **extraneous): warn_extraneous(extraneous) super(RungeKutta, self).__init__(fun, t0, y0, t_bound, vectorized, support_complex=True) self.y_old = None self.max_step = validate_max_step(max_step) self.rtol, self.atol = validate_tol(rtol, atol, self.n) self.f = self.fun(self.t, self.y) if first_step is None: self.h_abs = select_initial_step( self.fun, self.t, self.y, self.f, self.direction, self.error_estimator_order, self.rtol, self.atol) else: self.h_abs = validate_first_step(first_step, t0, t_bound) self.K = np.empty((self.n_stages + 1, self.n), dtype=self.y.dtype) self.error_exponent = -1 / (self.error_estimator_order + 1) self.h_previous = None def _estimate_error(self, K, h): return np.dot(K.T, self.E) * h def _estimate_error_norm(self, K, h, scale): return norm(self._estimate_error(K, h) / scale) def _step_impl(self): t = self.t y = self.y max_step = self.max_step rtol = self.rtol atol = self.atol min_step = 10 * np.abs(np.nextafter(t, self.direction * np.inf) - t) if self.h_abs > max_step: h_abs = max_step elif self.h_abs < min_step: h_abs = min_step else: h_abs = self.h_abs step_accepted = False step_rejected = False while not step_accepted: if h_abs < min_step: return False, self.TOO_SMALL_STEP h = h_abs * self.direction t_new = t + h if self.direction * (t_new - self.t_bound) > 0: t_new = self.t_bound h = t_new - t h_abs = np.abs(h) y_new, f_new = rk_step(self.fun, t, y, self.f, h, self.A, self.B, self.C, self.K) scale = atol + np.maximum(np.abs(y), np.abs(y_new)) * rtol error_norm = self._estimate_error_norm(self.K, h, scale) if error_norm < 1: if error_norm == 0: factor = MAX_FACTOR else: factor = min(MAX_FACTOR, SAFETY * error_norm ** self.error_exponent) if step_rejected: factor = min(1, factor) h_abs *= factor step_accepted = True else: h_abs *= max(MIN_FACTOR, SAFETY * error_norm ** self.error_exponent) step_rejected = True self.h_previous = h self.y_old = y self.t = t_new self.y = y_new self.h_abs = h_abs self.f = f_new return True, None def _dense_output_impl(self): Q = self.K.T.dot(self.P) return RkDenseOutput(self.t_old, self.t, self.y_old, Q) class RK23(RungeKutta): """Explicit Runge-Kutta method of order 3(2). This uses the Bogacki-Shampine pair of formulas [1]_. 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. 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 ndarray ``y``. It can either have shape (n,), then ``fun`` must return array_like with shape (n,). Or alternatively it can have shape (n, k), then ``fun`` must return 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). t0 : float Initial time. y0 : array_like, shape (n,) Initial state. t_bound : float Boundary time - the integration won't continue beyond it. It also determines the direction of the integration. first_step : float or None, optional Initial step size. Default is ``None`` which means that the algorithm should choose. 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`. vectorized : bool, optional Whether `fun` is implemented in a vectorized fashion. Default is False. Attributes ---------- n : int Number of equations. status : string Current status of the solver: 'running', 'finished' or 'failed'. t_bound : float Boundary time. direction : float Integration direction: +1 or -1. t : float Current time. y : ndarray Current state. t_old : float Previous time. None if no steps were made yet. step_size : float Size of the last successful step. None if no steps were made yet. nfev : int Number evaluations of the system's right-hand side. njev : int Number of evaluations of the Jacobian. Is always 0 for this solver as it does not use the Jacobian. nlu : int Number of LU decompositions. Is always 0 for this solver. References ---------- .. [1] P. Bogacki, L.F. Shampine, "A 3(2) Pair of Runge-Kutta Formulas", Appl. Math. Lett. Vol. 2, No. 4. pp. 321-325, 1989. """ order = 3 error_estimator_order = 2 n_stages = 3 C = np.array([0, 1/2, 3/4]) A = np.array([ [0, 0, 0], [1/2, 0, 0], [0, 3/4, 0] ]) B = np.array([2/9, 1/3, 4/9]) E = np.array([5/72, -1/12, -1/9, 1/8]) P = np.array([[1, -4 / 3, 5 / 9], [0, 1, -2/3], [0, 4/3, -8/9], [0, -1, 1]]) class RK45(RungeKutta): """Explicit Runge-Kutta method of order 5(4). This uses the Dormand-Prince pair of formulas [1]_. The error is controlled assuming accuracy of the fourth-order method accuracy, 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. 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). t0 : float Initial time. y0 : array_like, shape (n,) Initial state. t_bound : float Boundary time - the integration won't continue beyond it. It also determines the direction of the integration. first_step : float or None, optional Initial step size. Default is ``None`` which means that the algorithm should choose. 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`. vectorized : bool, optional Whether `fun` is implemented in a vectorized fashion. Default is False. Attributes ---------- n : int Number of equations. status : string Current status of the solver: 'running', 'finished' or 'failed'. t_bound : float Boundary time. direction : float Integration direction: +1 or -1. t : float Current time. y : ndarray Current state. t_old : float Previous time. None if no steps were made yet. step_size : float Size of the last successful step. None if no steps were made yet. nfev : int Number evaluations of the system's right-hand side. njev : int Number of evaluations of the Jacobian. Is always 0 for this solver as it does not use the Jacobian. nlu : int Number of LU decompositions. Is always 0 for this solver. 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. """ order = 5 error_estimator_order = 4 n_stages = 6 C = np.array([0, 1/5, 3/10, 4/5, 8/9, 1]) A = np.array([ [0, 0, 0, 0, 0], [1/5, 0, 0, 0, 0], [3/40, 9/40, 0, 0, 0], [44/45, -56/15, 32/9, 0, 0], [19372/6561, -25360/2187, 64448/6561, -212/729, 0], [9017/3168, -355/33, 46732/5247, 49/176, -5103/18656] ]) B = np.array([35/384, 0, 500/1113, 125/192, -2187/6784, 11/84]) E = np.array([-71/57600, 0, 71/16695, -71/1920, 17253/339200, -22/525, 1/40]) # Corresponds to the optimum value of c_6 from [2]_. P = np.array([ [1, -8048581381/2820520608, 8663915743/2820520608, -12715105075/11282082432], [0, 0, 0, 0], [0, 131558114200/32700410799, -68118460800/10900136933, 87487479700/32700410799], [0, -1754552775/470086768, 14199869525/1410260304, -10690763975/1880347072], [0, 127303824393/49829197408, -318862633887/49829197408, 701980252875 / 199316789632], [0, -282668133/205662961, 2019193451/616988883, -1453857185/822651844], [0, 40617522/29380423, -110615467/29380423, 69997945/29380423]]) class DOP853(RungeKutta): """Explicit Runge-Kutta method of order 8. This is a Python implementation of "DOP853" algorithm originally written in Fortran [1]_, [2]_. Note that this is not a literate translation, but the algorithmic core and coefficients are the same. Can be applied in the complex domain. 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). t0 : float Initial time. y0 : array_like, shape (n,) Initial state. t_bound : float Boundary time - the integration won't continue beyond it. It also determines the direction of the integration. first_step : float or None, optional Initial step size. Default is ``None`` which means that the algorithm should choose. 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`. vectorized : bool, optional Whether `fun` is implemented in a vectorized fashion. Default is False. Attributes ---------- n : int Number of equations. status : string Current status of the solver: 'running', 'finished' or 'failed'. t_bound : float Boundary time. direction : float Integration direction: +1 or -1. t : float Current time. y : ndarray Current state. t_old : float Previous time. None if no steps were made yet. step_size : float Size of the last successful step. None if no steps were made yet. nfev : int Number evaluations of the system's right-hand side. njev : int Number of evaluations of the Jacobian. Is always 0 for this solver as it does not use the Jacobian. nlu : int Number of LU decompositions. Is always 0 for this solver. References ---------- .. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential Equations I: Nonstiff Problems", Sec. II. .. [2] `Page with original Fortran code of DOP853 `_. """ n_stages = dop853_coefficients.N_STAGES order = 8 error_estimator_order = 7 A = dop853_coefficients.A[:n_stages, :n_stages] B = dop853_coefficients.B C = dop853_coefficients.C[:n_stages] E3 = dop853_coefficients.E3 E5 = dop853_coefficients.E5 D = dop853_coefficients.D A_EXTRA = dop853_coefficients.A[n_stages + 1:] C_EXTRA = dop853_coefficients.C[n_stages + 1:] def __init__(self, fun, t0, y0, t_bound, max_step=np.inf, rtol=1e-3, atol=1e-6, vectorized=False, first_step=None, **extraneous): super(DOP853, self).__init__(fun, t0, y0, t_bound, max_step, rtol, atol, vectorized, first_step, **extraneous) self.K_extended = np.empty((dop853_coefficients.N_STAGES_EXTENDED, self.n), dtype=self.y.dtype) self.K = self.K_extended[:self.n_stages + 1] def _estimate_error(self, K, h): # Left for testing purposes. err5 = np.dot(K.T, self.E5) err3 = np.dot(K.T, self.E3) denom = np.hypot(np.abs(err5), 0.1 * np.abs(err3)) correction_factor = np.ones_like(err5) mask = denom > 0 correction_factor[mask] = np.abs(err5[mask]) / denom[mask] return h * err5 * correction_factor def _estimate_error_norm(self, K, h, scale): err5 = np.dot(K.T, self.E5) / scale err3 = np.dot(K.T, self.E3) / scale err5_norm_2 = np.sum(err5**2) err3_norm_2 = np.sum(err3**2) denom = err5_norm_2 + 0.01 * err3_norm_2 return np.abs(h) * err5_norm_2 / np.sqrt(denom * len(scale)) def _dense_output_impl(self): K = self.K_extended h = self.h_previous for s, (a, c) in enumerate(zip(self.A_EXTRA, self.C_EXTRA), start=self.n_stages + 1): dy = np.dot(K[:s].T, a[:s]) * h K[s] = self.fun(self.t_old + c * h, self.y_old + dy) F = np.empty((dop853_coefficients.INTERPOLATOR_POWER, self.n), dtype=self.y_old.dtype) f_old = K[0] delta_y = self.y - self.y_old F[0] = delta_y F[1] = h * f_old - delta_y F[2] = 2 * delta_y - h * (self.f + f_old) F[3:] = h * np.dot(self.D, K) return Dop853DenseOutput(self.t_old, self.t, self.y_old, F) class RkDenseOutput(DenseOutput): def __init__(self, t_old, t, y_old, Q): super(RkDenseOutput, self).__init__(t_old, t) self.h = t - t_old self.Q = Q self.order = Q.shape[1] - 1 self.y_old = y_old def _call_impl(self, t): x = (t - self.t_old) / self.h if t.ndim == 0: p = np.tile(x, self.order + 1) p = np.cumprod(p) else: p = np.tile(x, (self.order + 1, 1)) p = np.cumprod(p, axis=0) y = self.h * np.dot(self.Q, p) if y.ndim == 2: y += self.y_old[:, None] else: y += self.y_old return y class Dop853DenseOutput(DenseOutput): def __init__(self, t_old, t, y_old, F): super(Dop853DenseOutput, self).__init__(t_old, t) self.h = t - t_old self.F = F self.y_old = y_old def _call_impl(self, t): x = (t - self.t_old) / self.h if t.ndim == 0: y = np.zeros_like(self.y_old) else: x = x[:, None] y = np.zeros((len(x), len(self.y_old)), dtype=self.y_old.dtype) for i, f in enumerate(reversed(self.F)): y += f if i % 2 == 0: y *= x else: y *= 1 - x y += self.y_old return y.T