Packaged code for PyPI by mullamm

README.md

@@ -7,13 +7,40 @@ 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
+This project solves the two-dimensional diffusion equation using finite difference methods. The diffusion equation is an important partial differential equation that describes the distribution of a quantity (such as temperature) over space and time. This package includes functions to simulate the diffusion process on a 2D grid and visualize the results.
 
 ## Installing the package
 
 ### Using pip3 to install from PyPI
+To install the package directly from PyPI, you can use pip3:
+
+```sh
+pip install -i https://test.pypi.org/simple/ mullamm_diffusion2d==0.0.1
+```
 
 ### Required dependencies
 
+The following dependencies are required to run this package:
+	•	numpy
+	•	matplotlib
+
+These dependencies will be installed automatically when you install the package using pip3.
+
+## Running this package
 ## Running this package
 
+To run the diffusion simulation, you can use the `solve` function provided in the package. Below is an example script that demonstrates how to use the package:
+
+```python
+from mullamm_diffusion2d import diffusion2d
+diffusion2d.solve()
+```
+
+# Run the simulation with default parameters
+solve()
+
+# Run the simulation with custom parameters
+solve(dx=0.05, dy=0.05, D=2.0)
+
 ## Citing
+Mahek Mulla (2024). mullamm_diffusion2d: A Python package for solving the 2D diffusion equation. Available at: https://github.com/mahek-mulla/diffusion2D.git

mullamm_diffusion2d.egg-info/PKG-INFO

@@ -0,0 +1,61 @@
+Metadata-Version: 2.1
+Name: mullamm_diffusion2d
+Version: 0.0.2
+Summary: A 2D diffusion solver example package
+Home-page: https://github.com/mahek-mulla/diffusion2D.git
+Author: Mahek Mulla
+Classifier: Programming Language :: Python :: 3
+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
+This project solves the two-dimensional diffusion equation using finite difference methods. The diffusion equation is an important partial differential equation that describes the distribution of a quantity (such as temperature) over space and time. This package includes functions to simulate the diffusion process on a 2D grid and visualize the results.
+
+## Installing the package
+
+### Using pip3 to install from PyPI
+To install the package directly from PyPI, you can use pip3:
+
+```sh
+pip install -i https://test.pypi.org/simple/ mullamm_diffusion2d==0.0.1
+```
+
+### Required dependencies
+
+The following dependencies are required to run this package:
+	•	numpy
+	•	matplotlib
+
+These dependencies will be installed automatically when you install the package using pip3.
+
+## Running this package
+## Running this package
+
+To run the diffusion simulation, you can use the `solve` function provided in the package. Below is an example script that demonstrates how to use the package:
+
+```python
+from mullamm_diffusion2d import diffusion2d
+diffusion2d.solve()
+```
+
+# Run the simulation with default parameters
+solve()
+
+# Run the simulation with custom parameters
+solve(dx=0.05, dy=0.05, D=2.0)
+
+## Citing
+Mahek Mulla (2024). mullamm_diffusion2d: A Python package for solving the 2D diffusion equation. Available at: https://github.com/mahek-mulla/diffusion2D.git

mullamm_diffusion2d.egg-info/SOURCES.txt

@@ -0,0 +1,12 @@
+LICENSE
+README.md
+setup.cfg
+setup.py
+mullamm_diffusion2d/__init__.py
+mullamm_diffusion2d/diffusion2d.py
+mullamm_diffusion2d/output.py
+mullamm_diffusion2d.egg-info/PKG-INFO
+mullamm_diffusion2d.egg-info/SOURCES.txt
+mullamm_diffusion2d.egg-info/dependency_links.txt
+mullamm_diffusion2d.egg-info/requires.txt
+mullamm_diffusion2d.egg-info/top_level.txt

mullamm_diffusion2d.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

mullamm_diffusion2d.egg-info/requires.txt

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

mullamm_diffusion2d.egg-info/top_level.txt

@@ -0,0 +1 @@
+mullamm_diffusion2d

mullamm_diffusion2d/diffusion2d.py

@@ -0,0 +1,78 @@
+"""
+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(dx=0.1, dy=0.1, D=4.0) :
+
+    # 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()
+
+    # 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,fig_counter,T_cold,T_hot,u,n,dt)
+
+
+    # Plot output figures
+    output_plots(fig, im)
+
+
+
+
+

mullamm_diffusion2d/output.py

@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+
+def create_plot(fig, fig_counter, T_cold, T_hot,u,n,dt):
+    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
+
+
+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()

setup.cfg

@@ -0,0 +1,19 @@
+[metadata]
+name = mullamm_diffusion2d
+version = 0.0.2
+author = Mahek Mulla
+description = A 2D diffusion solver example package
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/mahek-mulla/diffusion2D.git
+classifiers =
+    Programming Language :: Python :: 3
+    License :: OSI Approved :: MIT License
+    Operating System :: OS Independent
+
+[options]
+packages = find:
+python_requires = >=3.6
+install_requires =
+    numpy
+    matplotlib

setup.py

@@ -0,0 +1,6 @@
+# setup.py
+
+from setuptools import setup
+
+if __name__ == "__main__":
+    setup()