Packaged code for PyPI by tischlre

.gitignore

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+__pycache__/
+build/
+dist/
+*.egg-info/

README.md

@@ -8,12 +8,33 @@ The code used in this exercise is based on [Chapter 7 of the book "Learning Scie
 
 ## Project description
 
+This code simulates the 2D diffusion equation on a square domain, with the entire domain set to an initial temperature and a circular disc at the center maintained at a higher temperature.
+Using the Finite Difference Method, the code solves the diffusion equation, allowing the user to adjust the thermal diffusivity and initial conditions of the system.
+The simulation outputs four plots at different time points, vividly illustrating the progression of the diffusion process.
+
 ## Installing the package
 
 ### Using pip3 to install from PyPI
 
+```bash
+pip install --user --index-url https://test.pypi.org/simple/ tischlre_diffusion2d
+```
+
 ### Required dependencies
 
+```bash
+pip install numpy matplotlib
+```
+
 ## Running this package
 
+```python
+from tischlre_diffusion2d.diffusion2d import solve
+
+solve(dx = 0.1, dy = 0.1, D = 4)
+```
+
 ## Citing
+
+
+This was forked from https://github.com/Simulation-Software-Engineering/diffusion2D.

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel"]
+
+[project]
+name = "tischlre_diffusion2d"
+version = "0.0.1"
+description = "An example implementation for solving diffusion equation in 2D"
+readme = "README.md"
+requires-python = ">=3.6"
+license = { file = "LICENSE" }
+keywords = ["sse", "simulation", "diffusion"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+]
+dependencies = [
+    "numpy",
+    "matplotlib",
+]
+
+[project.urls]
+Homepage = "https://github.com/Simulation-Software-Engineering/diffusion2D"
+Repository = "https://github.com/Simulation-Software-Engineering/diffusion2D"
+
+[project.entry-points."simulation"]
+solve = "tischlre_diffusion2d:solve"

setup.py

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+from setuptools import setup, find_packages
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+if __name__ == "__main__":
+    setup(
+        long_description=long_description,
+        long_description_content_type="text/markdown",
+        packages=find_packages(exclude=["figures"]),
+    )

test.py

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+from tischlre_diffusion2d.diffusion2d import solve
+
+solve(dx = 0.1, dy = 0.1, D = 4)

tischlre_diffusion2d/diffusion2d.py

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+"""
+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
+
+from tischlre_diffusion2d.output import create_plot, output_plots
+
+def solve(dx = 0.1, dy = 0.1, D = 4):
+    # plate size, mm
+    w = h = 10.
+    # intervals in x-, y- directions, mm
+    dx = dy = 0.1
+    # Thermal diffusivity of steel, mm^2/s
+    D = 4.
+
+    # Initial cold temperature of square domain
+    T_cold = 300
+
+    # Initial hot temperature of circular disc at the center
+    T_hot = 700
+
+    # 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
+
+    def do_timestep(u_nm1, u, D, dt, dx2, dy2):
+        # Propagate with forward-difference in time, central-difference in space
+        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, D, dt, dx2, dy2)
+
+        # Create figure
+        if n in n_output:
+            im, fig_counter = create_plot(u, T_cold, T_hot, n, dt, fig_counter, fig)
+
+    # Plot output figures
+    output_plots(fig, im)

tischlre_diffusion2d/output.py

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+import matplotlib.pyplot as plt
+
+def create_plot(u, T_cold, T_hot, n, dt, fig_counter, fig):
+    fig_counter += 1
+    ax = fig.add_subplot(220 + fig_counter)
+    im = ax.imshow(u.copy(), 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, fig_counter
+
+def output_plots(fig, im):
+    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()