Packaged code for PyPI by singhaa

$HOME/.pypirc

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+[testpypi]
+  username = __token__
+  password = pypi-AgENdGVzdC5weXBpLm9yZwIkMGU4ZTM2YzgtMWU1My00ZjI5LWFhYjgtMjdlNjdjYmNkMzUxAAIqWzMsIjFkNDA2ODdmLTYyZGItNDhhOC1hYTJmLTk5MGJhYTY1NmFhNSJdAAAGIGuTgrGeCqS9KP_XKI-zH8aHHbnZ1e1pUL1ejEy_VqHS

adr1ja_diffusion2d.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: adr1ja_diffusion2d
+Version: 0.0.2
+Summary: A simulation package for solving the 2D diffusion equation using finite difference methods
+Author-email: Adrija <adrijasingh13@gmail.com>
+Keywords: diffusion,simulation,finite difference,scientific computing
+Classifier: Programming Language :: Python :: 3
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.10
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy>=2.1.0
+Requires-Dist: matplotlib>=3.9.0
+
+# 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

adr1ja_diffusion2d.egg-info/SOURCES.txt

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+LICENSE
+README.md
+pyproject.toml
+setup.py
+adr1ja_diffusion2d.egg-info/PKG-INFO
+adr1ja_diffusion2d.egg-info/SOURCES.txt
+adr1ja_diffusion2d.egg-info/dependency_links.txt
+adr1ja_diffusion2d.egg-info/requires.txt
+adr1ja_diffusion2d.egg-info/top_level.txt

adr1ja_diffusion2d.egg-info/dependency_links.txt

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+

adr1ja_diffusion2d.egg-info/requires.txt

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+numpy>=2.1.0
+matplotlib>=3.9.0

adr1ja_diffusion2d.egg-info/top_level.txt

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+

adr1ja_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 adr1ja_diffusion2d.output import create_plot, output_plots
+
+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
+
+def solve(dx=0.1, dy=0.1, D=4.0):
+    # Parameters
+    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))
+    nsteps = 101
+    n_output = [0, 10, 50, 100]
+
+    # Initial conditions
+    u0 = T_cold * np.ones((nx, ny))
+    u = u0.copy()
+    r = min(h, w) / 4.0
+    cx, cy = w / 2.0, h / 2.0
+    r2 = r ** 2
+    for i in range(nx):
+        for j in range(ny):
+            if (i * dx - cx) ** 2 + (j * dy - cy) ** 2 < r2:
+                u0[i, j] = T_hot
+
+    # Time loop and plotting
+    fig = plt.figure()
+    fig_counter = 0
+    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(u, T_cold, T_hot, dt, n, fig, fig_counter)
+
+    # Output all plots together
+    output_plots(im, fig)
+
+solve()
+
+
+

adr1ja_diffusion2d/output.py

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+import matplotlib.pyplot as plt
+
+def create_plot(u, T_cold, T_hot, dt, n, fig, fig_counter):
+    """Creates a single plot for a given 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('{:.1f} ms'.format(n * dt * 1000))
+    return im  # return the image for color bar usage later
+
+def output_plots(im, fig):
+    """Combines plots into a single figure and displays the color bar."""
+    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"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "adr1ja_diffusion2d"
+version = "0.0.2"
+description = "A simulation package for solving the 2D diffusion equation using finite difference methods"
+readme = "README.md"
+requires-python = ">=3.10"
+authors = [{name = "Adrija", email = "adrijasingh13@gmail.com"}]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "Operating System :: OS Independent",
+]
+keywords = ["diffusion", "simulation", "finite difference", "scientific computing"]
+dependencies = [
+    "numpy>=2.1.0",
+    "matplotlib>=3.9.0"
+]

setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name="adr1ja_diffusion2d",
+    version="0.0.2",
+    packages=find_packages(where="adr1ja_diffusion2d"),
+)