Final update before release

.gitignore

@@ -5,9 +5,3 @@ __pycache__/
 # images
 img/
 *.png
-
-# main.py
-main.py
-
-# plain text
-.txt

README.md

@@ -34,33 +34,29 @@ Suppose a model where $D = 0$. this can be interpreted as a under going convecti
 
 $$\frac{\partial u}{\partial t} = \mathbf{c} \cdot \nabla u$$
 
-## How to use
-
-
-
 ## Examples
 
 Consider a Diffusion Convection model in 2 dimension with the vector $\mathbf{c}$ having equal vector components and $D$ is constant.
 
-Let $\mathbf{c} = [-0.1, -0.1]$ and $D = 0.009$.
+Let $\mathbf{c} = [-0.1, -0.1]$ and $D = 0.009$. (Neumann boundary condition and 3d animations can be found in  [Two dimensional examples](examples/Two_Dimensional_Models.ipynb))
 
 ### Diffusion Convection Model
 
 Solution:
 
-![Diffusion Convection Model](./img/animation_diffusion_convection.gif)
+![!\[Diffusion Convection Model\](./img/animation_diffusion_convection.gif)](examples/img/animation_convection_diffusion_2dims_dirichlet_color.gif)
 
 ### Diffusion Model
 
 Solution:
 
-![Diffusion Model](./img/animation_diffusion.gif)
+![diffusion](examples/img/animation_diffusion_2dims_dirichlet_color.gif)
 
 ### Convection Model
 
 Solution:
 
-![Convection Model](./img/animation_convection.gif)
+![convection](examples/img/animation_convection_2dims_color.gif)
 
 ## Applications
 
@@ -70,6 +66,4 @@ Some applications of these models include:
 
 2. Tranportation of a fluids density distribution that is flowing uniformly.
 
-3. Fokker Plank Equation of a Stochastic Differential Equation with uniform drift and standard deviation.
-
-4. Shrodinger's Equation of a free particle in Quantum Mechanics.
+3. Fokker Plank Equation of a Stochastic Differential Equation with uniform drift and standard deviation.

animations.py

@@ -5,8 +5,6 @@
 
 # Animates the solution curve in 2 dimensions
 def animation_2(solution: np.ndarray, X: np.ndarray, Y: np.ndarray, xlab: str, ylab: str, zlab: str, title: str, zlim: tuple, fps: float, frn: int, filename: str):
-    plt.rcParams["figure.figsize"] = [10, 10]
-    plt.rcParams["figure.autolayout"] = True
 
     def change_plot(frame_num, solution, plot):
         plot[0].remove()
@@ -71,20 +69,32 @@ def animate(i):
 
 
 # Animates the solution curve in 2 dimensions but as a color plot
-def animation_color(solution: np.ndarray, fps: float, frn: int, filename: str):
+def animation_color(solution: np.ndarray, xlab: str, ylab: str, title: str, xlim_: tuple, ylim_: tuple, fps: float, frn: int, filename: str):
     # Create the figure and axis objects
     fig, ax = plt.subplots()
 
     # Set the axis limits
-    ax.set_xlim(-5, 5)
-    ax.set_ylim(-5, 5)
+    ax.set_xlim(xlim_[0], xlim_[1])
+    ax.set_ylim(ylim_[0], ylim_[1])
 
     # Define the plotting function
     def update_plot(i):
         im.set_array(solution[i, :, :])
 
     # Create the initial plot
-    im = ax.imshow(solution[0, :, :], extent=[-5, 5, -5, 5], cmap='viridis')
+    im = ax.imshow(solution[0, :, :], extent=[xlim_[0], xlim_[1], ylim_[0], ylim_[1]], cmap='viridis')
+
+    # x label
+    if xlab is not None:
+        plt.xlabel(xlab)
+
+    # y label
+    if ylab is not None:
+        plt.ylabel(ylab)
+
+    # title
+    if title is not None:
+        plt.title(title)
 
     # Create the animation object
     anim = animation.FuncAnimation(fig, update_plot, frames=frn, interval=1/fps)