Packaged code for PyPI by chenla

.gitignore

@@ -0,0 +1,209 @@
+# Created by https://www.toptal.com/developers/gitignore/api/macos,python
+# Edit at https://www.toptal.com/developers/gitignore?templates=macos,python
+
+### macOS ###
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+
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+# pyenv
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+# End of https://www.toptal.com/developers/gitignore/api/macos,python

README.md

@@ -8,12 +8,39 @@ The code used in this exercise is based on [Chapter 7 of the book "Learning Scie
 
 ## 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
 
+
 ### Using pip3 to install from PyPI
 
+```bash
+pip3 install chenla_diffusion2D
+```
+
 ### Required dependencies
 
+* Check if your system has 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.
+
 ## Running this package
 
+You could run the package via `diffusion2d.solve()` after import. This will generate 4 plots with the default parameters `dx=0.1, dy=0.1, D=4.`, where `dx` and `dy` are intervals in x-, y- directions (mm), and `D` is the thermal diffusivity of steel (mm^2/s).
+
+```python
+from chenla_diffusion2D import diffusion2d
+
+diffusion2d.solve()
+```
+
+You could also assign the values of `dx`, `dy` and `D` by yourself.
+
+```python
+from chenla_diffusion2D import diffusion2d
+
+diffusion2d.solve(dx=0.5, dy=0.5, D=16)
+```
+
 ## Citing

chenla_diffusion2D/diffusion2d.py

@@ -0,0 +1,77 @@
+"""
+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.):
+    """
+    dx, dy: intervals in x-, y- directions, mm
+    D: Thermal diffusivity of steel, mm^2/s
+    """
+    # 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, u.copy(), n*dt*1000, 220+fig_counter, T_cold, T_hot)
+
+    # Plot output figures
+    output_plots(fig, im)

chenla_diffusion2D/output.py

@@ -0,0 +1,20 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def create_plot(fig, data, timestamp, subplot_pos, vmin, vmax):
+    """This is the function to create one plot for a particular time stamp.
+    """
+    ax = fig.add_subplot(subplot_pos)
+    im = ax.imshow(data, cmap=plt.get_cmap('hot'), vmin=vmin, vmax=vmax)  # image for color bar axes
+    ax.set_axis_off()
+    ax.set_title('{:.1f} ms'.format(timestamp))
+    return im
+
+def output_plots(fig, im):
+    """This is the function to output all the four plots as one figure.
+    """
+    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()