Packaged code for PyPI by zipfeljs

README.md

@@ -7,13 +7,29 @@ Please follow the instructions in [pypi_exercise.md](https://github.com/Simulati
 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/).
 
 ## Project description
+Diffusion2D solves the diffusion equation in 2D over a square domain which is at a certain temperature and a circular disc at the center which is at a higher temperature. This code solves the diffusion equation using the Finite Difference Method. The thermal diffusivity and initial conditions of the system can be changed by the user. The code produces four plots at various timepoints of the simulation. The diffusion process can be clearly observed in these plots.
+If you are interested in the theoretical background of the code, please have a look in [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
 
-### Using pip3 to install from PyPI
 
+### Using pip3 to install from PyPI
+```
+pip install -i https://test.pypi.org/simple/ zipfeljs-diffusion2d==0.0.1
+```
 ### Required dependencies
+Check if your system has Python version >= 3.6 and update it if it is older than 3.6.
 
-## Running this package
+```
+python --version
+```
+install pip, matplotlib and numpy
 
+## Running this package
+Run the code using python and running the solve function in diffusion2d.py
+it has three parameters with these standard values:
+```
+dx = dy = 0.1
+D = 4.
+```
 ## Citing

pyproject.toml

@@ -0,0 +1,16 @@
+[build-system]
+requires = ["setuptools", "wheel"]
+
+[project]
+version = "0.0.2"
+name = "zipfeljs_diffusion2d"
+description = "Diffusion2D solves the diffusion equation in 2D over a square domain which is at a certain temperature and a circular disc at the center which is at a higher temperature"
+readme = "README.md"
+keywords = ["SimulationSoftwareEngenieering", "UniStuttgart","MasterStudent"]
+classifiers=[
+        "Programming Language :: Python :: 3"
+    ]
+dependencies = [
+    "requests",
+    'importlib-metadata; python_version<"3.8"',
+]

setup.py

@@ -0,0 +1,9 @@
+from setuptools import setup
+import setuptools
+
+setup(
+    author="Johannes_Zipfel",
+    url="https://github.com/johzip/diffusion2D/blob/main/diffusion2d.py",
+    package_dir={"": "zipfeljs_diffusion2d"},
+    packages=setuptools.find_packages(where="zipfeljs_diffusion2d")
+)

zipfeljs_diffusion2d/diffusion2d.py

@@ -0,0 +1,76 @@
+"""
+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 output import create_plot, output_plots
+
+def solve(
+    # intervals in x-, y- directions, mm
+    dx = 0.1, dy = 0.1 ,
+    # Thermal diffusivity of steel, mm^2/s
+    D = 4. ):
+
+    # plate size, mm
+    w = h = 10. 
+
+    # 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()
+
+
+    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(T_cold, T_hot, dt, u, fig_counter, fig, n)
+
+    # Plot output figures
+    output_plots(fig, im)

zipfeljs_diffusion2d/output.py

@@ -0,0 +1,17 @@
+
+import matplotlib.pyplot as plt
+
+def create_plot(T_cold, T_hot, dt, u, fig_counter, fig, n): 
+    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)  # image for color bar axes
+    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()