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)[source]¶
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(t), len(y0))
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)
Other Parameters: ml, mu : int
If either of these are not None or non-negative, then the Jacobian is assumed to be banded. These give the number of lower and upper non-zero diagonals in this banded matrix. For the banded case, Dfun should return a matrix whose columns contain the non-zero bands (starting with the lowest diagonal). Thus, the return matrix from Dfun should have shape len(y0) * (ml + mu + 1) when ml >=0 or mu >=0.
rtol, atol : float
The input parameters rtol and atol determine the error control performed by the solver. The solver will control the vector, e, of estimated local errors in y, according to an inequality of the form max-norm of (e / ewt) <= 1, where ewt is a vector of positive error weights computed as ewt = rtol * abs(y) + atol. rtol and atol can be either vectors the same length as y or scalars. Defaults to 1.49012e-8.
tcrit : ndarray
Vector of critical points (e.g. singularities) where integration care should be taken.
h0 : float, (0: solver-determined)
The step size to be attempted on the first step.
hmax : float, (0: solver-determined)
The maximum absolute step size allowed.
hmin : float, (0: solver-determined)
The minimum absolute step size allowed.
ixpr : bool
Whether to generate extra printing at method switches.
mxstep : int, (0: solver-determined)
Maximum number of (internally defined) steps allowed for each integration point in t.
mxhnil : int, (0: solver-determined)
Maximum number of messages printed.
mxordn : int, (0: solver-determined)
Maximum order to be allowed for the non-stiff (Adams) method.
mxords : int, (0: solver-determined)
Maximum order to be allowed for the stiff (BDF) method.
