Packaged code for PyPI by mamoyn

README.md

@@ -1,19 +1,41 @@
 # diffusion2D
 
-## Instructions for students
+## Project description
 
-Please follow the instructions in [pypi_exercise.md](https://github.com/Simulation-Software-Engineering/Lecture-Material/blob/main/03_building_and_packaging/pypi_exercise.md).
+This package provides a solution for the 2D diffusion equation over a square domain using the Finite Difference Method. The domain starts at a uniform temperature, with a circular disc at the center at a higher temperature. Users can modify thermal diffusivity and initial conditions to simulate various scenarios. The output includes four plots showing the diffusion process at different timepoints.
 
-The code used in this exercise is based on [Chapter 7 of the book "Learning Scientific Programming with Python"](https://scipython.com/book/chapter-7-matplotlib/examples/the-two-dimensional-diffusion-equation/).
+## Installing the package
 
-## Project description
+To install the package, use:
 
-## Installing the package
+```bash
+pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple mamoyn_diffusion2d
 
-### Using pip3 to install from PyPI
+```
 
 ### Required dependencies
 
+- Python (>=3.6)
+- numpy
+- matplotlib
+
 ## Running this package
 
+To run the simulation with default parameters, execute:
+
+```bash
+solve
+```
+
+For custom parameters, import and call the `solve` function:
+
+```python
+from mamoyn_diffusion2d import diffusion2d
+diffusion2d.solve(dx=0.05, dy=0.05, D=2.0)
+```
+
 ## Citing
+
+If using this package, please cite:
+
+> Yonatan Mamo. *mamoyn_diffusion2d: A Python Package for Solving 2D Diffusion Equations.* Version 0.0.1, 2024. Available at [https://test.pypi.org/project/mamoyn_diffusion2d/](https://test.pypi.org/project/mamoyn_diffusion2d/).

mamoyn_diffusion2d/diffusion2d.py

@@ -0,0 +1,69 @@
+"""
+Solving the two-dimensional diffusion equation
+
+Example acquired from https://scipython.com/book/chapter-7-matplotlib/examples/the-two-dimensional-diffusion-equation/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt  # Import pyplot AFTER setting the backend
+from .output import create_plot, output_plots
+
+# solve the 2D diffusion equation
+def solve(dx=0.1, dy=0.1, D=4.0):
+    # plate size, mm
+    w = h = 10.
+    # Thermal diffusivity of steel, mm^2/s
+    T_cold = 300  # Initial cold temperature of square domain
+    T_hot = 700   # Initial hot temperature of circular disc at the center
+
+    # Number of discrete mesh points in X and Y directions
+    nx, ny = int(w / dx), int(h / dy)
+
+    # Computing a stable time step
+    dx2, dy2 = dx * dx, dy * dy
+    dt = dx2 * dy2 / (2 * D * (dx2 + dy2))
+
+    print("dt = {}".format(dt))
+
+    u0 = T_cold * np.ones((nx, ny))
+    u = u0.copy()
+
+    # Initial conditions - circle of radius r centred at (cx,cy) (mm)
+    r = min(h, w) / 4.0
+    cx = w / 2.0
+    cy = h / 2.0
+    r2 = r ** 2
+    for i in range(nx):
+        for j in range(ny):
+            p2 = (i * dx - cx) ** 2 + (j * dy - cy) ** 2
+            if p2 < r2:
+                u0[i, j] = T_hot
+
+    # propagate with forward-difference in time, central-difference in space
+    def do_timestep(u_nm1, u):
+        u[1:-1, 1:-1] = u_nm1[1:-1, 1:-1] + D * dt * (
+                (u_nm1[2:, 1:-1] - 2 * u_nm1[1:-1, 1:-1] + u_nm1[:-2, 1:-1]) / dx2
+                + (u_nm1[1:-1, 2:] - 2 * u_nm1[1:-1, 1:-1] + u_nm1[1:-1, :-2]) / dy2)
+
+        u_nm1 = u.copy()
+        return u_nm1, u
+
+    # Number of timesteps
+    nsteps = 101
+    # Output 4 figures at these timesteps
+    n_output = [0, 10, 50, 100]
+    fig_counter = 0
+    fig = plt.figure()
+
+    # Time loop
+    for n in range(nsteps):
+        u0, u = do_timestep(u0, u)
+
+        # Create figure
+        if n in n_output:
+            fig_counter += 1
+            im = create_plot(fig, u.copy(), n, dt, T_cold, T_hot, fig_counter)
+
+    # Plot output figures
+    output_plots(fig, im, T_cold, T_hot)
+

mamoyn_diffusion2d/output.py

@@ -0,0 +1,17 @@
+import matplotlib.pyplot as plt
+
+# function to create a plot for a single timestamp
+def create_plot(fig, data, n, dt, T_cold, T_hot, fig_counter):
+    ax = fig.add_subplot(220 + fig_counter)
+    im = ax.imshow(data, cmap=plt.get_cmap('hot'), vmin=T_cold, vmax=T_hot)
+    ax.set_axis_off()
+    ax.set_title('{:.1f} ms'.format(n * dt * 1000))
+    return im
+
+# function to output all plots as one figure
+def output_plots(fig, im, T_cold, T_hot):
+    fig.subplots_adjust(right=0.85)
+    cbar_ax = fig.add_axes([0.9, 0.15, 0.03, 0.7])
+    cbar_ax.set_xlabel('$T$ / K', labelpad=20)
+    fig.colorbar(im, cax=cbar_ax)
+    plt.show()

setup.cfg

@@ -0,0 +1,28 @@
+[metadata]
+name = mamoyn_diffusion2d
+version = 0.0.2
+author = Yonatan Mamo
+author_email = girmayonatan86@gmail.com
+description = A Python package to solve the 2D diffusion equation, designed for Simulation Software Engineering.
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/Simulation-Software-Engineering/diffusion2D
+classifiers =
+    Programming Language :: Python :: 3
+    License :: OSI Approved :: MIT License
+    Operating System :: OS Independent
+    Topic :: Scientific/Engineering
+    Intended Audience :: Education
+    Intended Audience :: Developers
+    Environment :: Console
+
+[options]
+packages = find:
+python_requires = >=3.6
+install_requires =
+    numpy
+    matplotlib
+
+[options.entry_points]
+console_scripts =
+    solve = mamoyn_diffusion2d.diffusion2d:solve

setup.py

@@ -0,0 +1,4 @@
+from setuptools import setup
+
+if __name__ == '__main__':
+    setup()