py_an_ode¶

Use Python Runge-Kutta Method to Solve an ODE¶

Syntax¶

      ode = cmpad.an_ode( *like_numpy* )
      ode . setup ( option )
      yf = ode ( x )

Prototype¶

class an_ode :
   def __init__(self, like_numpy) :
      self.like_numpy = like_numpy
   #
   def option(self) :
      return self.option
   #
   def domain(self) :
      return self.n_arg
   #
   def range(self) :
      return self.n_arg
   #
   def setup(self, option) :
      assert type(option) == dict
      assert type( option['n_arg'] ) == int
      assert type( option['n_other'] ) == int
      assert option['n_arg'] > 0
      assert option['n_other'] > 0

Algorithm¶

This is a python implementation of the an_ode Algorithm .

like_numpy¶

This is a like_numpy class. It is used to vectorize the rk4_step algorithm.

ode¶

The object ode corresponding to fun_obj in the function object interface.

n_arg¶

see n_arg . This is the number of elements that are computed by one like_numpy operation in the r4k_step algorithm

n_other¶

see n_other .

x¶

This is the Parameter Vector that appears in the ODE.

yf¶

The return value yf is \(y(t)\) at \(t = 2\) .

Example¶

xam_an_ode.py contains an example and test of an_ode .

Source Code¶

an_ode.py displays the source code for this algorithm.