Packaged code for PyPI by ahmedsa

.gitignore

@@ -0,0 +1,3 @@
+__pycache__
+dist
+src/ahmedsa_diffusion2d.egg-info

README.md

@@ -1,19 +1,36 @@
 # diffusion2D
 
-## Instructions for students
-
-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).
-
-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
+# Project description
+A 2D diffusion equation solver with visualization output that used matplotlib and numpy to plot 3 graphs. The project can take arguements for values of dx, dy and D to compute and visualize the 2D diffusion and use matplotlib to plot the figures.
 
 ## Installing the package
+You can use pip to directly install the package or build it from source using pip from the tar.gz/.whl package from [TestPyPi](link)
 
 ### Using pip3 to install from PyPI
+To install the package, simply run:
+```console
+pip install -i https://test.pypi.org/simple/ ahmedsa-diffusion2d
+```
 
 ### Required dependencies
+The required dependencies are:
+- Python (version >= 3)
+- pip
+- NumPy
+- Matplotlib
+If you plan to build from source, you also need to install the **build** pip package.
 
 ## Running this package
+To run the package, you can use the following command:
+```console
+python3 diffusion2d.py
+```
+Or if you prefer to pass values of dx, dy and D (respectively), you can pass them as arguements as follows:
+```console
+python3 diffusion2d.py solve <dx> <dy> <D>
+```
 
 ## Citing
+- [Chapter 7 of the book "Learning Scientific Programming with Python"](https://scipython.com/book/chapter-7-matplotlib/examples/the-two-dimensional-diffusion-equation/)
+- [Simulation Service Engineering Exercise](https://github.com/Simulation-Software-Engineering/diffusion2D)
+

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel", "build"]
+
+[project]
+name = "ahmedsa_diffusion2d"
+description = "A 2D diffusion equation solver with visualization output"
+authors = [
+    { name="Syed Mustafa Ahmed", email="[email protected]" }
+]
+readme = "README.md"
+keywords = ["diffusion2D", "solver"]
+classifiers = [
+    "Programming Language :: Python :: 3"
+]
+dependencies = [
+    "matplotlib",
+    "numpy"
+]
+requires-python = ">=3.0"
+version = "0.0.2"

setup.py

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+from setuptools import setup
+
+if __name__ == "__main__":
+  setup()

diffusion2d.py → src/diffusion2d.py

@@ -4,8 +4,10 @@
 Example acquired from https://scipython.com/book/chapter-7-matplotlib/examples/the-two-dimensional-diffusion-equation/
 """
 
+import sys
 import numpy as np
 import matplotlib.pyplot as plt
+from output import create_plot, output_plots
 
 # plate size, mm
 w = h = 10.
@@ -45,37 +47,54 @@
 
 
 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:
-        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))
-
-# Plot output figures
-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()
+# Solver function
+def solve(dx=0.1, dy=0.1, D=4.0):
+    w = h = 10.0
+    T_cold = 300
+    T_hot = 700
+    nx, ny = int(w / dx), int(h / dy)
+    dx2, dy2 = dx * dx, dy * dy
+    dt = dx2 * dy2 / (2 * D * (dx2 + dy2))
+    u0 = T_cold * np.ones((nx, ny))
+    u = u0.copy()
+    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
+
+    nsteps = 101
+    n_output = [0, 10, 50, 100]
+    fig = plt.figure()
+    fig_counter = 0
+    im = None
+
+    for n in range(nsteps):
+        u0, u = do_timestep(u0, u, D, dt, dx2, dy2)
+        if n in n_output:
+            fig_counter += 1
+            im = create_plot(fig, u, T_cold, T_hot, dt, n, fig_counter)
+
+    output_plots(fig, im)
+
+# Running the solve function as a test
+if __name__ == "__main__":
+    if len(sys.argv) < 4:
+        print("Usage: python3 diffusion2d.py <dx> <dy> <D>")
+    else:
+        dx = float(sys.argv[2])
+        dy = float(sys.argv[3])
+        D = float(sys.argv[4])           
+        print(f'dx={dx}, dy={dy}, D={D}')
+
+        solve(dx, dy, D)

src/output.py

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+import matplotlib.pyplot as plt
+
+def create_plot(fig, u, T_cold, T_hot, dt, time_step, fig_counter):
+    ax = fig.add_subplot(220 + fig_counter)
+    im = ax.imshow(u, cmap=plt.get_cmap('hot'), vmin=T_cold, vmax=T_hot)
+    ax.set_axis_off()
+    ax.set_title(f'{time_step * dt * 1000:.1f} ms')
+    return im
+
+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()