class_docstr: A generic interface class to numeric integrators.
  
  Solve an equation system :math:`y'(t) = f(t,y)` with (optional) ``jac = df/dy``.
  
  *Note*: The first two arguments of ``f(t, y, ...)`` are in the
  opposite order of the arguments in the system definition function used
  by `scipy.integrate.odeint`.
  
  Parameters
  ----------
  f : callable ``f(t, y, *f_args)``
      Right-hand side of the differential equation. t is a scalar,
      ``y.shape == (n,)``.
      ``f_args`` is set by calling ``set_f_params(*args)``.
      `f` should return a scalar, array or list (not a tuple).
  jac : callable ``jac(t, y, *jac_args)``, optional
      Jacobian of the right-hand side, ``jac[i,j] = d f[i] / d y[j]``.
      ``jac_args`` is set by calling ``set_jac_params(*args)``.
  
  Attributes
  ----------
  t : float
      Current time.
  y : ndarray
      Current variable values.
  
  See also
  --------
  odeint : an integrator with a simpler interface based on lsoda from ODEPACK
  quad : for finding the area under a curve
  
  Notes
  -----
  Available integrators are listed below. They can be selected using
  the `set_integrator` method.
  
  "vode"
  
      Real-valued Variable-coefficient Ordinary Differential Equation
      solver, with fixed-leading-coefficient implementation. It provides
      implicit Adams method (for non-stiff problems) and a method based on
      backward differentiation formulas (BDF) (for stiff problems).
  
      Source: http://www.netlib.org/ode/vode.f
  
      .. warning::
  
         This integrator is not re-entrant. You cannot have two `ode`
         instances using the "vode" integrator at the same time.
  
      This integrator accepts the following parameters in `set_integrator`
      method of the `ode` class:
  
      - atol : float or sequence
        absolute tolerance for solution
      - rtol : float or sequence
        relative tolerance for solution
      - lband : None or int
      - uband : None or int
        Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+uband.
        Setting these requires your jac routine to return the jacobian
        in packed format, jac_packed[i-j+uband, j] = jac[i,j]. The
        dimension of the matrix must be (lband+uband+1, len(y)).
      - method: 'adams' or 'bdf'
        Which solver to use, Adams (non-stiff) or BDF (stiff)
      - with_jacobian : bool
        This option is only considered when the user has not supplied a
        Jacobian function and has not indicated (by setting either band)
        that the Jacobian is banded. In this case, `with_jacobian` specifies
        whether the iteration method of the ODE solver's correction step is
        chord iteration with an internally generated full Jacobian or
        functional iteration with no Jacobian.
      - nsteps : int
        Maximum number of (internally defined) steps allowed during one
        call to the solver.
      - first_step : float
      - min_step : float
      - max_step : float
        Limits for the step sizes used by the integrator.
      - order : int
        Maximum order used by the integrator,
        order <= 12 for Adams, <= 5 for BDF.
  
  "zvode"
  
      Complex-valued Variable-coefficient Ordinary Differential Equation
      solver, with fixed-leading-coefficient implementation. It provides
      implicit Adams method (for non-stiff problems) and a method based on
      backward differentiation formulas (BDF) (for stiff problems).
  
      Source: http://www.netlib.org/ode/zvode.f
  
      .. warning::
  
         This integrator is not re-entrant. You cannot have two `ode`
         instances using the "zvode" integrator at the same time.
  
      This integrator accepts the same parameters in `set_integrator`
      as the "vode" solver.
  
      .. note::
  
          When using ZVODE for a stiff system, it should only be used for
          the case in which the function f is analytic, that is, when each f(i)
          is an analytic function of each y(j). Analyticity means that the
          partial derivative df(i)/dy(j) is a unique complex number, and this
          fact is critical in the way ZVODE solves the dense or banded linear
          systems that arise in the stiff case. For a complex stiff ODE system
          in which f is not analytic, ZVODE is likely to have convergence
          failures, and for this problem one should instead use DVODE on the
          equivalent real system (in the real and imaginary parts of y).
  
  "lsoda"
  
      Real-valued Variable-coefficient Ordinary Differential Equation
      solver, with fixed-leading-coefficient implementation. It provides
      automatic method switching between implicit Adams method (for non-stiff
      problems) and a method based on backward differentiation formulas (BDF)
      (for stiff problems).
  
      Source: http://www.netlib.org/odepack
  
      .. warning::
  
         This integrator is not re-entrant. You cannot have two `ode`
         instances using the "lsoda" integrator at the same time.
  
      This integrator accepts the following parameters in `set_integrator`
      method of the `ode` class:
  
      - atol : float or sequence
        absolute tolerance for solution
      - rtol : float or sequence
        relative tolerance for solution
      - lband : None or int
      - uband : None or int
        Jacobian band width, jac[i,j] != 0 for i-lband <= j <= i+uband.
        Setting these requires your jac routine to return the jacobian
        in packed format, jac_packed[i-j+uband, j] = jac[i,j].
      - with_jacobian : bool
        *Not used.*
      - nsteps : int
        Maximum number of (internally defined) steps allowed during one
        call to the solver.
      - first_step : float
      - min_step : float
      - max_step : float
        Limits for the step sizes used by the integrator.
      - max_order_ns : int
        Maximum order used in the nonstiff case (default 12).
      - max_order_s : int
        Maximum order used in the stiff case (default 5).
      - max_hnil : int
        Maximum number of messages reporting too small step size (t + h = t)
        (default 0)
      - ixpr : int
        Whether to generate extra printing at method switches (default False).
  
  "dopri5"
  
      This is an explicit runge-kutta method of order (4)5 due to Dormand &
      Prince (with stepsize control and dense output).
  
      Authors:
  
          E. Hairer and G. Wanner
          Universite de Geneve, Dept. de Mathematiques
          CH-1211 Geneve 24, Switzerland
          e-mail:  ernst.hairer@math.unige.ch, gerhard.wanner@math.unige.ch
  
      This code is described in [HNW93]_.
  
      This integrator accepts the following parameters in set_integrator()
      method of the ode class:
  
      - atol : float or sequence
        absolute tolerance for solution
      - rtol : float or sequence
        relative tolerance for solution
      - nsteps : int
        Maximum number of (internally defined) steps allowed during one
        call to the solver.
      - first_step : float
      - max_step : float
      - safety : float
        Safety factor on new step selection (default 0.9)
      - ifactor : float
      - dfactor : float
        Maximum factor to increase/decrease step size by in one step
      - beta : float
        Beta parameter for stabilised step size control.
      - verbosity : int
        Switch for printing messages (< 0 for no messages).
  
  "dop853"
  
      This is an explicit runge-kutta method of order 8(5,3) due to Dormand
      & Prince (with stepsize control and dense output).
  
      Options and references the same as "dopri5".
  
  Examples
  --------
  
  A problem to integrate and the corresponding jacobian:
  
  >>> from scipy.integrate import ode
  >>>
  >>> y0, t0 = [1.0j, 2.0], 0
  >>>
  >>> def f(t, y, arg1):
  ...     return [1j*arg1*y[0] + y[1], -arg1*y[1]**2]
  >>> def jac(t, y, arg1):
  ...     return [[1j*arg1, 1], [0, -arg1*2*y[1]]]
  
  The integration:
  
  >>> r = ode(f, jac).set_integrator('zvode', method='bdf')
  >>> r.set_initial_value(y0, t0).set_f_params(2.0).set_jac_params(2.0)
  >>> t1 = 10
  >>> dt = 1
  >>> while r.successful() and r.t < t1:
  ...     print(r.t+dt, r.integrate(r.t+dt))
  1 [-0.71038232+0.23749653j  0.40000271+0.j        ]
  2.0 [0.19098503-0.52359246j 0.22222356+0.j        ]
  3.0 [0.47153208+0.52701229j 0.15384681+0.j        ]
  4.0 [-0.61905937+0.30726255j  0.11764744+0.j        ]
  5.0 [0.02340997-0.61418799j 0.09523835+0.j        ]
  6.0 [0.58643071+0.339819j 0.08000018+0.j      ]
  7.0 [-0.52070105+0.44525141j  0.06896565+0.j        ]
  8.0 [-0.15986733-0.61234476j  0.06060616+0.j        ]
  9.0 [0.64850462+0.15048982j 0.05405414+0.j        ]
  10.0 [-0.38404699+0.56382299j  0.04878055+0.j        ]
  
  References
  ----------
  .. [HNW93] E. Hairer, S.P. Norsett and G. Wanner, Solving Ordinary
      Differential Equations i. Nonstiff Problems. 2nd edition.
      Springer Series in Computational Mathematics,
      Springer-Verlag (1993)
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docstr: Set extra parameters for user-supplied function jac.
docstr_tok: ["Set","extra","parameters","for","user-supplied","function","jac","."]
code: def set_jac_params(self, *args):
      """Set extra parameters for user-supplied function jac."""
      self.jac_params = args
      return self
  
imports: import re
  import warnings
  from numpy import asarray, array, zeros, isscalar, real, imag, vstack
  from .None import _vode
  from .None import _dop
  from .None import _lsoda
label_desc: Use image node_id 9 for calling the ode obj's underlying member method code with example usage: obj.set_jac_params() and returns: self