Packaged code for PyPI by niyati

.gitignore

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

README.md

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 # 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
+This code 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.
 
 ## Installing the package
+To install the `diffusion2D` package from TestPyPI and PyPI, you can use the following `pip` commands:
 
+### Using pip3 to install from TestPyPI
+```bash
+pip3 install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple <username>_diffusion2d
+```
 ### Using pip3 to install from PyPI
+```bash
+pip3 install <username>_diffusion2d
+```
 
 ### Required dependencies
+The following dependencies are required to run the code:
+- Python version >= 3.6 and update it if it is older than 3.6.
+- Install pip, build, and Twine.
+- Install NumPy and Matplotlib with pip..
+
+You can install them using:
+```bash
+pip install numpy matplotlib twine
+```
+Alternatively, if you are installing the package directly, the dependencies will be installed automatically.
 
 ## Running this package
+Once the package is installed, you can use the solve() function to simulate the 2D diffusion process. We can run the code either interactively in a Python shell or in a Python script. 
+If you choose Python Shell and run the following command.
+```python
+>>> from niyati_diffusion2d import diffusion2d
+>>> diffusion2d.solve()
+```
+
+you can also build using the following command:
+
+You can install them using:
+```python
+>>> python3 -m build
+```
+
 
 ## Citing
+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).
+
+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/)

niyati_diffusion2d.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: niyati_diffusion2d
+Version: 0.1
+Summary: diffusion2D
+Author-email: Niyati <[email protected]>
+License: BSD-3-Clause
+Keywords: one,two
+Classifier: Programming Language :: Python :: 3
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: importlib-metadata; python_version < "3.4"
+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
+
+## Installing the package
+
+### Using pip3 to install from PyPI
+
+### Required dependencies
+
+## Running this package
+
+## Citing

niyati_diffusion2d.egg-info/SOURCES.txt

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+LICENSE
+README.md
+pyproject.toml
+niyati_diffusion2d/diffusion2d.py
+niyati_diffusion2d.egg-info/PKG-INFO
+niyati_diffusion2d.egg-info/SOURCES.txt
+niyati_diffusion2d.egg-info/dependency_links.txt
+niyati_diffusion2d.egg-info/requires.txt
+niyati_diffusion2d.egg-info/top_level.txt

niyati_diffusion2d.egg-info/dependency_links.txt

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+

niyati_diffusion2d.egg-info/requires.txt

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+numpy
+matplotlib
+
+[:python_version < "3.4"]
+importlib-metadata

niyati_diffusion2d.egg-info/top_level.txt

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+niyati_diffusion2d

niyati_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 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:
+            #creating 1 plot taking parameter fig, fig_counter, T_cold, T_hot, u, n dt
+            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))
+            ax,im = create_plot(fig_counter, fig, u, T_cold, T_hot, n,dt)
+
+    # 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()
+    output_plots(fig,im)

output.py

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+import matplotlib.pyplot as plt
+
+def create_plot(fig_counter, fig, u,T_cold,T_hot, 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 ax,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()
+

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "niyati_diffusion2d"
+authors = [
+    {name = "Niyati", email = "[email protected]"},
+]
+description = "diffusion2D"
+readme = "README.md"
+requires-python = ">=3.6"
+keywords = ["one", "two"]
+license = {text = "BSD-3-Clause"}
+classifiers = [
+    "Programming Language :: Python :: 3",
+]
+dependencies = [
+    'importlib-metadata; python_version<"3.4"',
+     'numpy', 
+     'matplotlib',
+]
+version="0.1"
+

test-script.py

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+import niyati_diffusion2d.diffusion2d
+
+
+niyati_diffusion2d.diffusion2d.solve()