Packaged code for PyPI by youssemd

README.md

@@ -8,12 +8,18 @@ The code used in this exercise is based on [Chapter 7 of the book "Learning Scie
 
 ## Project description
 
-## Installing the package
+`diffusion2D` is a Python package that numerically solves the 2D diffusion equation using the Finite Difference Method. The package simulates the process of heat diffusion in a square domain, with a circular heat source at the center. The simulation produces visual plots showing the temperature distribution at various time steps.
 
-### Using pip3 to install from PyPI
+### Features:
+- Solves the 2D diffusion equation numerically for a given domain and initial conditions.
+- Visualizes the temperature distribution over time using Matplotlib.
+- Parameters such as grid spacing (`dx`, `dy`) and thermal diffusivity (`D`) can be customized.
 
-### Required dependencies
+---
 
-## Running this package
+## Installing the package
 
-## Citing
+### Using pip3 to install from PyPI
+Once the package is uploaded to PyPI, you can install it using pip:
+```bash
+pip install --user --index-url https://test.pypi.org/simple/ youssemd_diffusion2d

setup.cfg

@@ -0,0 +1,28 @@
+[metadata]
+name = youssemd_diffusion2d
+version = 0.0.1
+author = Mohamed Youssef
+author_email = [email protected]
+description = A Python package for solving the 2D diffusion equation using Finite Difference Method
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/MohamedAlyLoutfy/diffusion2D.git
+classifiers =
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.6
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    License :: OSI Approved :: MIT License
+    Operating System :: OS Independent
+
+[options]
+packages = find:
+python_requires = >=3.6
+install_requires =
+    numpy
+    matplotlib
+
+[options.package_data]
+* = *.txt, *.md

setup.py

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

youssemd_diffusion2d.egg-info/PKG-INFO

@@ -0,0 +1,46 @@
+Metadata-Version: 2.1
+Name: youssemd_diffusion2d
+Version: 0.0.1
+Summary: A Python package for solving the 2D diffusion equation using Finite Difference Method
+Home-page: https://github.com/MohamedAlyLoutfy/diffusion2D.git
+Author: Mohamed Youssef
+Author-email: [email protected]
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy
+Requires-Dist: matplotlib
+
+# 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
+
+`diffusion2D` is a Python package that numerically solves the 2D diffusion equation using the Finite Difference Method. The package simulates the process of heat diffusion in a square domain, with a circular heat source at the center. The simulation produces visual plots showing the temperature distribution at various time steps.
+
+### Features:
+- Solves the 2D diffusion equation numerically for a given domain and initial conditions.
+- Visualizes the temperature distribution over time using Matplotlib.
+- Parameters such as grid spacing (`dx`, `dy`) and thermal diffusivity (`D`) can be customized.
+
+---
+
+## Installing the package
+
+### Using pip3 to install from PyPI
+Once the package is uploaded to PyPI, you can install it using pip:
+```bash
+pip install --user --index-url https://test.pypi.org/simple/ youssemd_diffusion2d

youssemd_diffusion2d.egg-info/SOURCES.txt

@@ -0,0 +1,12 @@
+LICENSE
+README.md
+setup.cfg
+setup.py
+youssemd_diffusion2d/__init__.py
+youssemd_diffusion2d/diffusion2d.py
+youssemd_diffusion2d/output.py
+youssemd_diffusion2d.egg-info/PKG-INFO
+youssemd_diffusion2d.egg-info/SOURCES.txt
+youssemd_diffusion2d.egg-info/dependency_links.txt
+youssemd_diffusion2d.egg-info/requires.txt
+youssemd_diffusion2d.egg-info/top_level.txt

youssemd_diffusion2d.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

youssemd_diffusion2d.egg-info/requires.txt

@@ -0,0 +1,2 @@
+numpy
+matplotlib

youssemd_diffusion2d.egg-info/top_level.txt

@@ -0,0 +1 @@
+youssemd_diffusion2d

youssemd_diffusion2d/diffusion2d.py

@@ -0,0 +1,68 @@
+"""
+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
+from youssemd_diffusion2d.output import create_plot, output_plots
+import matplotlib.pyplot as plt
+
+
+def solve(dx=0.1, dy=0.1, D=4.0):
+    # Plate size, mm
+    w = h = 10.0
+    # Thermal diffusivity of steel, mm^2/s
+    T_cold = 300
+    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(f"dt = {dt}")
+
+    u0 = T_cold * np.ones((nx, ny))
+    u = u0.copy()
+
+    # Initial conditions - circle of radius r centered 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:
+            fig_counter += 1
+            im = create_plot(fig, u, T_cold, T_hot, n, dt, fig_counter)
+
+    # Plot output figures
+    output_plots(fig, im)

youssemd_diffusion2d/output.py

@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+
+
+def create_plot(fig, u, T_cold, T_hot, time_step, dt, fig_counter):
+    """Creates a single plot for a specific time step."""
+    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(f'{time_step * dt * 1000:.1f} ms')
+    return im
+
+
+def output_plots(fig, im):
+    """Finalizes and displays the combined plots."""
+    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()