Plug-and-play standalone library for solving 2D Poisson equations. Useful tool in scientific computing prototyping, image and video processing, computer graphics.
- Solves the Poisson equation on sqaure or non-square rectangular grids.
- Solves the Poisson equation on regions with arbitrary shape.
- Supports arbitrary boundary and interior conditions using
sympyfunction experssions ornumpyarrays. - Supports Dirichlet, Neumann, or mixed boundary conditions.
This package is only used to solve 2D Poisson equations. If you are looking for a general purpose and optimized PDE library, you might want to checkout the FEniCSx project.
Import necessary libraries. poissonpy utilizes numpy and sympy greatly, so its best to import both:
import numpy as np
from sympy import sin, cos
from sympy.abc import x, y
from poissonpy import functional, utils, sovlersIn the following examples, we use a ground truth function to create a mock Poisson equation and compare the solver's solution with the analytical solution.
Define functions using sympy function expressions or numpy arrays:
f_expr = sin(x) + cos(y) # create sympy function expression
laplacian_expr = functional.get_sp_laplacian_expr(f_expr) # create sympy laplacian function expression
f = functional.get_sp_function(f_expr) # create sympy function
laplacian = functional.get_sp_function(laplacian_expr) # create sympy functionDefine interior and Dirichlet boundary conditions:
interior = laplacian
boundary = {
"left": (f, "dirichlet"),
"right": (f, "dirichlet"),
"top": (f, "dirichlet"),
"bottom": (f, "dirichlet")
}Initialize solver and solve Poisson equation:
solver = Poisson2DRectangle(((-2*np.pi, -2*np.pi), (2*np.pi, 2*np.pi)),
interior, boundary, X=100, Y=100)
solution = solver.solve()Plot solution and ground truth:
poissonpy.plot_3d(solver.x_grid, solver.y_grid, solution)
poissonpy.plot_3d(solver.x_grid, solver.y_grid, f(solver.x_grid, solver.y_grid))| Solution | Ground truth | Error |
|---|---|---|
 |
 |
 |
You can also define Neumann boundary conditions by specifying neumann_x and neumann_y in the boundary condition parameter.
x_derivative_expr = functional.get_sp_derivative_expr(f_expr, x)
y_derivative_expr = functional.get_sp_derivative_expr(f_expr, y)
interior = laplacian
boundary = {
"left": (f, "dirichlet"),
"right": (functional.get_sp_function(x_derivative_expr), "neumann_x"),
"top": (f, "dirichlet"),
"bottom": (functional.get_sp_function(y_derivative_expr), "neumann_y")
}| Solution | Ground truth | Error |
|---|---|---|
 |
 |
 |
If the boundary condition is purely Neumann, then the solution is not unique. Naively solving the Poisson equation gives bad results. In this case, you can set the zero_mean paramter to True, such that the solver finds a zero-mean solution.
solver = solvers.Poisson2DRectangle(
((-2*np.pi, -2*np.pi), (2*np.pi, 2*np.pi)), interior, boundary,
X=100, Y=100, zero_mean=True)zero_mean=False |
zero_mean=True |
Ground truth |
|---|---|---|
 |
 |
|
It's also straightforward to define a Laplace equation - we simply set the interior laplacian value to 0. In the following example, we set the boundary values to be spatially-varying periodic functions.
interior = 0 # laplace equation form
left = poissonpy.get_2d_sympy_function(sin(y))
right = poissonpy.get_2d_sympy_function(sin(y))
top = poissonpy.get_2d_sympy_function(sin(x))
bottom = poissonpy.get_2d_sympy_function(sin(x))
boundary = {
"left": (left, "dirichlet"),
"right": (right, "dirichlet"),
"top": (top, "dirichlet"),
"bottom": (bottom, "dirichlet")
}Solve the Laplace equation:
solver = Poisson2DRectangle(
((-2*np.pi, -2*np.pi), (2*np.pi, 2*np.pi)), interior, boundary, 100, 100)
solution = solver.solve()
poissonpy.plot_3d(solver.x_grid, solver.y_grid, solution, "solution")
poissonpy.plot_2d(solution, "solution")| 3D surface plot | 2D heatmap |
|---|---|
 |
 |
Use the Poisson2DRegion class to solve the Poisson eqaution on a arbitrary-shaped function domain. poissonpy can be seamlessly integrated in gradient-domain image processing algorithms.
The following is an example where poissonpy is used to implement the image cloning algorithm proposed in Poisson Image Editing by Perez et al., 2003. See examples/poisson_image_editing.py for more details.
# compute laplacian of interpolation function
Gx_src, Gy_src = functional.get_np_gradient(source)
Gx_target, Gy_target = functional.get_np_gradient(target)
G_src_mag = (Gx_src**2 + Gy_src**2)**0.5
G_target_mag = (Gx_target**2 + Gy_target**2)**0.5
Gx = np.where(G_src_mag > G_target_mag, Gx_src, Gx_target)
Gy = np.where(G_src_mag > G_target_mag, Gy_src, Gy_target)
Gxx, _ = functional.get_np_gradient(Gx, forward=False)
_, Gyy = functional.get_np_gradient(Gy, forward=False)
laplacian = Gxx + Gyy
# solve interpolation function
solver = solvers.Poisson2DRegion(mask, laplacian, target)
solution = solver.solve()
# alpha-blend interpolation and target function
blended = mask * solution + (1 - mask) * target
Another example of using poissonpy to implement flash artifacts and reflection removal, using the algorithm proposed in Removing Photography Artifacts using Gradient Projection and Flash-Exposure Sampling by Agrawal et al. 2005. See examples/flash_noflash.py for more details.
Gx_a, Gy_a = functional.get_np_gradient(ambient)
Gx_f, Gy_f = functional.get_np_gradient(flash)
# gradient projection
t = (Gx_a * Gx_f + Gy_a * Gy_f) / (Gx_a**2 + Gy_a**2 + 1e-8)
Gx_f_proj = t * Gx_a
Gy_f_proj = t * Gy_a
# compute laplacian (div of gradient)
lap = functional.get_np_div(Gx_f_proj, Gy_f_proj)
# integrate laplacian field
solver = solvers.Poisson2DRegion(mask, lap, flash)
res = solver.solve()
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