select_initial_step(fun, t0, y0, f0, direction, order, rtol, atol)

The algorithm is described in .

Parameters

fun : callable

Right-hand side of the system.

t0 : float

Initial value of the independent variable.

y0 : ndarray, shape (n,)

Initial value of the dependent variable.

f0 : ndarray, shape (n,)

Initial value of the derivative, i.e., fun(t0, y0) .

direction : float

Integration direction.

order : float

Error estimator order. It means that the error controlled by the algorithm is proportional to ``step_size ** (order + 1)`.

rtol : float

Desired relative tolerance.

atol : float

Desired absolute tolerance.

Returns

h_abs : float

Absolute value of the suggested initial step.

Empirically select a good initial step.

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /scipy/integrate/_ivp/common.py#64
type: <class 'function'>
Commit: