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# scipy.integrate.odeint

`scipy.integrate.``odeint`(func, y0, t, args=(), Dfun=None, col_deriv=0, full_output=0, ml=None, mu=None, rtol=None, atol=None, tcrit=None, h0=0.0, hmax=0.0, hmin=0.0, ixpr=0, mxstep=0, mxhnil=0, mxordn=12, mxords=5, printmessg=0)

  Integrate a system of ordinary differential equations.

  Solve a system of ordinary differential equations using lsoda from the FORTRAN library odepack.

  Solves the initial value problem for stiff or non-stiff systems of first order ode-s:

      dy/dt = func(y,t0,...)

  where y can be a vector.

  Parameters: | func : callable(y, t0, ...) Computes the derivative of y at t0. y0 : array Initial condition on y (can be a vector). t : array A sequence of time points for which to solve for y. The initial value point should be the first element of this sequence. args : tuple Extra arguments to pass to function. Dfun : callable(y, t0, ...) Gradient (Jacobian) of func. col_deriv : boolean True if Dfun defines derivatives down columns (faster), otherwise Dfun should define derivatives across rows. full_output : boolean True if to return a dictionary of optional outputs as the second output printmessg : boolean Whether to print the convergence message
  > Returns: | y : array, shape (len(y0), len(t)) Array containing the value of y for each desired time in t, with the initial value y0 in the first row. infodict : dict, only returned if full_output == True Dictionary containing additional output information
  > key | meaning
  > --- | ---
  > ‘hu’ | vector of step sizes successfully used for each time step.
  > ‘tcur’ | vector with the value of t reached for each time step. (will always be at least as large as the input times).
  > ‘tolsf’ | vector of tolerance scale factors, greater than 1.0, computed when a request for too much accuracy was detected.
  > ‘tsw’ | value of t at the time of the last method switch (given for each time step)
  > ‘nst’ | cumulative number of time steps
  > ‘nfe’ | cumulative number of function evaluations for each time step
  > ‘nje’ | cumulative number of jacobian evaluations for each time step
  > ‘nqu’ | a vector of method orders for each successful step.
  > ‘imxer’ | index of the component of largest magnitude in the weighted local error vector (e / ewt) on an error return, -1 otherwise.
  > ‘lenrw’ | the length of the double work array required.
  > ‘leniw’ | the length of integer work array required.
  > ‘mused’ | a vector of method indicators for each successful time step: 1: adams (nonstiff), 2: bdf (stiff)

  See also

  `ode`
    a more object-oriented integrator based on VODE
  `quad`
    for finding the area under a curve

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