image-handler/image-handler10.0/venv/lib/site-packages/mpmath/calculus/odes.py

from bisect import bisect
from ..libmp.backend import xrange

class ODEMethods(object):
    pass

def ode_taylor(ctx, derivs, x0, y0, tol_prec, n):
    h = tol = ctx.ldexp(1, -tol_prec)
    dim = len(y0)
    xs = [x0]
    ys = [y0]
    x = x0
    y = y0
    orig = ctx.prec
    try:
        ctx.prec = orig*(1+n)
        # Use n steps with Euler's method to get
        # evaluation points for derivatives
        for i in range(n):
            fxy = derivs(x, y)
            y = [y[i]+h*fxy[i] for i in xrange(len(y))]
            x += h
            xs.append(x)
            ys.append(y)
        # Compute derivatives
        ser = [[] for d in range(dim)]
        for j in range(n+1):
            s = [0]*dim
            b = (-1) ** (j & 1)
            k = 1
            for i in range(j+1):
                for d in range(dim):
                    s[d] += b * ys[i][d]
                b = (b * (j-k+1)) // (-k)
                k += 1
            scale = h**(-j) / ctx.fac(j)
            for d in range(dim):
                s[d] = s[d] * scale
                ser[d].append(s[d])
    finally:
        ctx.prec = orig
    # Estimate radius for which we can get full accuracy.
    # XXX: do this right for zeros
    radius = ctx.one
    for ts in ser:
        if ts[-1]:
            radius = min(radius, ctx.nthroot(tol/abs(ts[-1]), n))
    radius /= 2  # XXX
    return ser, x0+radius

def odefun(ctx, F, x0, y0, tol=None, degree=None, method='taylor', verbose=False):
    r"""
    Returns a function `y(x) = [y_0(x), y_1(x), \ldots, y_n(x)]`
    that is a numerical solution of the `n+1`-dimensional first-order
    ordinary differential equation (ODE) system

    .. math ::

        y_0'(x) = F_0(x, [y_0(x), y_1(x), \ldots, y_n(x)])

        y_1'(x) = F_1(x, [y_0(x), y_1(x), \ldots, y_n(x)])

        \vdots

        y_n'(x) = F_n(x, [y_0(x), y_1(x), \ldots, y_n(x)])

    The derivatives are specified by the vector-valued function
    *F* that evaluates
    `[y_0', \ldots, y_n'] = F(x, [y_0, \ldots, y_n])`.
    The initial point `x_0` is specified by the scalar argument *x0*,
    and the initial value `y(x_0) =  [y_0(x_0), \ldots, y_n(x_0)]` is
    specified by the vector argument *y0*.

    For convenience, if the system is one-dimensional, you may optionally
    provide just a scalar value for *y0*. In this case, *F* should accept
    a scalar *y* argument and return a scalar. The solution function
    *y* will return scalar values instead of length-1 vectors.

    Evaluation of the solution function `y(x)` is permitted
    for any `x \ge x_0`.

    A high-order ODE can be solved by transforming it into first-order
    vector form. This transformation is described in standard texts
    on ODEs. Examples will also be given below.

    **Options, speed and accuracy**

    By default, :func:`~mpmath.odefun` uses a high-order Taylor series
    method. For reasonably well-behaved problems, the solution will
    be fully accurate to within the working precision. Note that
    *F* must be possible to evaluate to very high precision
    for the generation of Taylor series to work.

    To get a faster but less accurate solution, you can set a large
    value for *tol* (which defaults roughly to *eps*). If you just
    want to plot the solution or perform a basic simulation,
    *tol = 0.01* is likely sufficient.

    The *degree* argument controls the degree of the solver (with
    *method='taylor'*, this is the degree of the Taylor series
    expansion). A higher degree means that a longer step can be taken
    before a new local solution must be generated from *F*,
    meaning that fewer steps are required to get from `x_0` to a given
    `x_1`. On the other hand, a higher degree also means that each
    local solution becomes more expensive (i.e., more evaluations of
    *F* are required per step, and at higher precision).

    The optimal setting therefore involves a tradeoff. Generally,
    decreasing the *degree* for Taylor series is likely to give faster
    solution at low precision, while increasing is likely to be better
    at higher precision.

    The function
    object returned by :func:`~mpmath.odefun` caches the solutions at all step
    points and uses polynomial interpolation between step points.
    Therefore, once `y(x_1)` has been evaluated for some `x_1`,
    `y(x)` can be evaluated very quickly for any `x_0 \le x \le x_1`.
    and continuing the evaluation up to `x_2 > x_1` is also fast.

    **Examples of first-order ODEs**

    We will solve the standard test problem `y'(x) = y(x), y(0) = 1`
    which has explicit solution `y(x) = \exp(x)`::

        >>> from mpmath import *
        >>> mp.dps = 15; mp.pretty = True
        >>> f = odefun(lambda x, y: y, 0, 1)
        >>> for x in [0, 1, 2.5]:
        ...     print((f(x), exp(x)))
        ...
        (1.0, 1.0)
        (2.71828182845905, 2.71828182845905)
        (12.1824939607035, 12.1824939607035)

    The solution with high precision::

        >>> mp.dps = 50
        >>> f = odefun(lambda x, y: y, 0, 1)
        >>> f(1)
        2.7182818284590452353602874713526624977572470937
        >>> exp(1)
        2.7182818284590452353602874713526624977572470937

    Using the more general vectorized form, the test problem
    can be input as (note that *f* returns a 1-element vector)::

        >>> mp.dps = 15
        >>> f = odefun(lambda x, y: [y[0]], 0, [1])
        >>> f(1)
        [2.71828182845905]

    :func:`~mpmath.odefun` can solve nonlinear ODEs, which are generally
    impossible (and at best difficult) to solve analytically. As
    an example of a nonlinear ODE, we will solve `y'(x) = x \sin(y(x))`
    for `y(0) = \pi/2`. An exact solution happens to be known
    for this problem, and is given by
    `y(x) = 2 \tan^{-1}\left(\exp\left(x^2/2\right)\right)`::

        >>> f = odefun(lambda x, y: x*sin(y), 0, pi/2)
        >>> for x in [2, 5, 10]:
        ...     print((f(x), 2*atan(exp(mpf(x)**2/2))))
        ...
        (2.87255666284091, 2.87255666284091)
        (3.14158520028345, 3.14158520028345)
        (3.14159265358979, 3.14159265358979)

    If `F` is independent of `y`, an ODE can be solved using direct
    integration. We can therefore obtain a reference solution with
    :func:`~mpmath.quad`::

        >>> f = lambda x: (1+x**2)/(1+x**3)
        >>> g = odefun(lambda x, y: f(x), pi, 0)
        >>> g(2*pi)
        0.72128263801696
        >>> quad(f, [pi, 2*pi])
        0.72128263801696

    **Examples of second-order ODEs**

    We will solve the harmonic oscillator equation `y''(x) + y(x) = 0`.
    To do this, we introduce the helper functions `y_0 = y, y_1 = y_0'`
    whereby the original equation can be written as `y_1' + y_0' = 0`. Put
    together, we get the first-order, two-dimensional vector ODE

    .. math ::

        \begin{cases}
        y_0' = y_1 \\
        y_1' = -y_0
        \end{cases}

    To get a well-defined IVP, we need two initial values. With
    `y(0) = y_0(0) = 1` and `-y'(0) = y_1(0) = 0`, the problem will of
    course be solved by `y(x) = y_0(x) = \cos(x)` and
    `-y'(x) = y_1(x) = \sin(x)`. We check this::

        >>> f = odefun(lambda x, y: [-y[1], y[0]], 0, [1, 0])
        >>> for x in [0, 1, 2.5, 10]:
        ...     nprint(f(x), 15)
        ...     nprint([cos(x), sin(x)], 15)
        ...     print("---")
        ...
        [1.0, 0.0]
        [1.0, 0.0]
        ---
        [0.54030230586814, 0.841470984807897]
        [0.54030230586814, 0.841470984807897]
        ---
        [-0.801143615546934, 0.598472144103957]
        [-0.801143615546934, 0.598472144103957]
        ---
        [-0.839071529076452, -0.54402111088937]
        [-0.839071529076452, -0.54402111088937]
        ---

    Note that we get both the sine and the cosine solutions
    simultaneously.

    **TODO**

    * Better automatic choice of degree and step size
    * Make determination of Taylor series convergence radius
      more robust
    * Allow solution for `x < x_0`
    * Allow solution for complex `x`
    * Test for difficult (ill-conditioned) problems
    * Implement Runge-Kutta and other algorithms

    """
    if tol:
        tol_prec = int(-ctx.log(tol, 2))+10
    else:
        tol_prec = ctx.prec+10
    degree = degree or (3 + int(3*ctx.dps/2.))
    workprec = ctx.prec + 40
    try:
        len(y0)
        return_vector = True
    except TypeError:
        F_ = F
        F = lambda x, y: [F_(x, y[0])]
        y0 = [y0]
        return_vector = False
    ser, xb = ode_taylor(ctx, F, x0, y0, tol_prec, degree)
    series_boundaries = [x0, xb]
    series_data = [(ser, x0, xb)]
    # We will be working with vectors of Taylor series
    def mpolyval(ser, a):
        return [ctx.polyval(s[::-1], a) for s in ser]
    # Find nearest expansion point; compute if necessary
    def get_series(x):
        if x < x0:
            raise ValueError
        n = bisect(series_boundaries, x)
        if n < len(series_boundaries):
            return series_data[n-1]
        while 1:
            ser, xa, xb = series_data[-1]
            if verbose:
                print("Computing Taylor series for [%f, %f]" % (xa, xb))
            y = mpolyval(ser, xb-xa)
            xa = xb
            ser, xb = ode_taylor(ctx, F, xb, y, tol_prec, degree)
            series_boundaries.append(xb)
            series_data.append((ser, xa, xb))
            if x <= xb:
                return series_data[-1]
    # Evaluation function
    def interpolant(x):
        x = ctx.convert(x)
        orig = ctx.prec
        try:
            ctx.prec = workprec
            ser, xa, xb = get_series(x)
            y = mpolyval(ser, x-xa)
        finally:
            ctx.prec = orig
        if return_vector:
            return [+yk for yk in y]
        else:
            return +y[0]
    return interpolant

ODEMethods.odefun = odefun

if __name__ == "__main__":
    import doctest
    doctest.testmod()