Python/maths/euler_modified.py
Aman kanojiya 9b4cb05ee5
Modified Euler's Method (#5258)
* Magnitude and Angle 

Core function to find Magnitude and Angle of two Given Vector

* Magnitude and Angle with Doctest

added Doctest to the functions

* Update linear_algebra/src/lib.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Update linear_algebra/src/lib.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Changes done 

and Magnitude and Angle Issues

* black

* Modified Euler's Method

Adding Modified Euler's method, which was the further change to a Euler method and known for better accuracy to the given value

* Modified Euler's Method (changed the typing of function)

Modified function is used for better accuracy

* Link added

Added link to an explanation as per Contributions Guidelines

* Resolving Pre-Commit error

* Pre-Commit Error Resolved

* Pre-Commit Error import statement Change

* Removed Import Math

* import math built issue

* adding space pre-commit error

* statement sorter for doc

Co-authored-by: Christian Clauss <cclauss@me.com>
2021-10-14 12:53:03 +02:00

55 lines
1.4 KiB
Python

from typing import Callable
import numpy as np
def euler_modified(
ode_func: Callable, y0: float, x0: float, step_size: float, x_end: float
) -> np.array:
"""
Calculate solution at each step to an ODE using Euler's Modified Method
The Euler is straightforward to implement, but can't give accurate solutions.
So, they Proposed some changes to improve the accuracy
https://en.wikipedia.org/wiki/Euler_method
Arguments:
ode_func -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
stepsize -- the increment value for x
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f1(x, y):
... return -2*x*(y**2)
>>> y = euler_modified(f1, 1.0, 0.0, 0.2, 1.0)
>>> y[-1]
0.503338255442106
>>> import math
>>> def f2(x, y):
... return -2*y + (x**3)*math.exp(-2*x)
>>> y = euler_modified(f2, 1.0, 0.0, 0.1, 0.3)
>>> y[-1]
0.5525976431951775
"""
N = int(np.ceil((x_end - x0) / step_size))
y = np.zeros((N + 1,))
y[0] = y0
x = x0
for k in range(N):
y_get = y[k] + step_size * ode_func(x, y[k])
y[k + 1] = y[k] + (
(step_size / 2) * (ode_func(x, y[k]) + ode_func(x + step_size, y_get))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()