pdepy 1.0.1

A Finite-Difference PDE solver.

Overview

Disclaimer: Use at your own risk. I have a bachelor’s degree in applied and computational mathematics, but never worked professionally in the field.

A python 3 library for solving initial and boundary value problems of some linear partial differential equations using finite-difference methods:

• Laplace

  • implicit central

• Parabolic

  • explicit central

  • explicit upwind

  • implicit central

  • implicit upwind

• Wave

  • explicit

  • implicit

Getting Started

Installing

pip install pdepy

Examples

Laplace’s Equation:

import numpy as np
from pdepy import laplace

xn, xf, yn, yf = 30, 3., 40, 4.

x = np.linspace(0, xf, xn+1)
y = np.linspace(0, yf, yn+1)

f = lambda x, y: (x-1)**2 - (y-2)**2
bound_x0 = f(0, y)
bound_xf = f(xf, y)
bound_y0 = f(x, 0)
bound_yf = f(x, yf)

axis  = (x, y)
conds = (bound_x0, bound_xf, bound_y0, bound_yf)

laplace.solve(axis, conds, method='ic')

Parabolic Equation:

import numpy as np
from pdepy import parabolic

xn, xf, yn, yf = 40, 4., 50, 0.5

x = np.linspace(0, xf, xn+1)
y = np.linspace(0, yf, yn+1)

init  = x**2 - 4*x + 5
bound = 5 * np.exp(-y)

p, q, r, s = 1, 1, -3, 3

axis   = (x, y)
conds  = (init, bound, bound)
params = (p, q, r, s)

parabolic.solve(axis, params, conds, method='iu')

Wave Equation:

import numpy as np
from pdepy import wave

xn, xf, yn, yf = 40, 1., 40, 1.

x = np.linspace(0, xf, xn+1)
y = np.linspace(0, yf, yn+1)

d_init = 1
init   = x * (1-x)
bound  = y * (1-y)

axis  = (x, y)
conds = (d_init, init, bound, bound)

wave.solve(axis, conds, method='i')

Developing and Testing

# Install locally.
pip install -e .[dev]

# Lint the code.
fab lint

# Run unit tests without coverage.
fab test

# Run unit tests and print coverage reports on the screen.
fab coverage

# Run unit tests and create coverage reports in html files.
fab coverage:html

Packaging and Distributing

Do not forget to update the version field in setup.py.

# Install "twine" if not installed yet.
pip install twine

# Package and distribute to PyPI.
fab distribute

More here.