Fix dynamic function change

app.py

@@ -7,8 +7,8 @@ import gradio as gr
 import plotly.graph_objs as go
 
 glob_k = 0.0025
-glob_a = -2.
-glob_b = 4.
+glob_a = -2.0
+glob_b = 4.0
 glob_c = 7.5
 
 
@@ -26,7 +26,7 @@ def clear_npz():
                 print(f"Failed to delete {file_path}. Reason: {e}")
 
 
-def complex_heat_eq_solution(x, t, k=glob_k, a=glob_a, b=glob_b, c=glob_c):
+def complex_heat_eq_solution(x, t, k, a, b, c):
     global glob_k, glob_a, glob_b, glob_c
     glob_k, glob_a, glob_b, glob_c = k, a, b, c
     return (
@@ -37,6 +37,7 @@ def complex_heat_eq_solution(x, t, k=glob_k, a=glob_a, b=glob_b, c=glob_c):
 
 
 def plot_heat_equation(m, approx_type):
+    global glob_k, glob_a, glob_b, glob_c
     # Define grid dimensions
     n_x = 32  # Fixed spatial grid resolution
     n_t = 50
@@ -54,7 +55,7 @@ def plot_heat_equation(m, approx_type):
     X, T = np.meshgrid(x, t)
 
     # Compute the real solution over the grid
-    U_real = complex_heat_eq_solution(X, T)
+    U_real = complex_heat_eq_solution(X, T, glob_k, glob_a, glob_b, glob_c)
 
     # Compute the selected approximation
     U_approx = np.zeros_like(U_real)
@@ -172,7 +173,7 @@ def plot_errors(m, approx_type):
     X, T = np.meshgrid(x, t)
 
     # Compute the real solution over the grid
-    U_real = complex_heat_eq_solution(X, T)
+    U_real = complex_heat_eq_solution(X, T, glob_k, glob_a, glob_b, glob_c)
 
     # Compute the selected approximation
     U_approx = np.zeros_like(U_real)
@@ -261,13 +262,14 @@ def plot_errors(m, approx_type):
 
 
 def generate_data(n_x=32, n_t=50):
+    global glob_k, glob_a, glob_b, glob_c
     """Generate training data."""
     x = np.linspace(0, 1, n_x)  # spatial points
     t = np.linspace(0, 5, n_t)  # temporal points
     X, T = np.meshgrid(x, t)
     a_train = np.c_[X.ravel(), T.ravel()]  # shape (n_x * n_t, 2)
     u_train = complex_heat_eq_solution(
-        a_train[:, 0], a_train[:, 1]
+        a_train[:, 0], a_train[:, 1], glob_k, glob_a, glob_b, glob_c
     )  # shape (n_x * n_t,)
     return a_train, u_train, x, t
 
@@ -321,6 +323,7 @@ def polyfit2d(x, y, z, kx=3, ky=3, order=None):
 
 
 def train_coefficients(m, kernel):
+    global glob_k, glob_a, glob_b, glob_c
     # Start time for training
     start_time = time.time()
 
@@ -328,6 +331,8 @@ def train_coefficients(m, kernel):
     n_x, n_t = 32, 50
     a_train, u_train, x, t = generate_data(n_x, n_t)
 
+    print("in train coeffs: ", glob_k)
+
     # Define random features
     theta = np.column_stack(
         (
@@ -340,7 +345,7 @@ def train_coefficients(m, kernel):
     Phi = design_matrix(a_train, theta, kernel)
     alpha = learn_coefficients(Phi, u_train)
     # Validate and animate results
-    u_real = np.array([complex_heat_eq_solution(x, t_i) for t_i in t])
+    u_real = np.array([complex_heat_eq_solution(x, t_i, glob_k, glob_a, glob_b, glob_c) for t_i in t])
     a_test = np.c_[np.meshgrid(x, t)[0].ravel(), np.meshgrid(x, t)[1].ravel()]
     u_approx = approximate_solution(a_test, alpha, theta, kernel).reshape(n_t, n_x)
 
@@ -364,6 +369,8 @@ def plot_function(k, a, b, c):
 
     glob_k, glob_a, glob_b, glob_c = k, a, b, c
 
+    print(glob_k, k)
+
     x = np.linspace(0, 1, 100)
     t = np.linspace(0, 5, 500)
     X, T = np.meshgrid(x, t)  # Create the mesh grid
@@ -434,10 +441,11 @@ def plot_all(m, kernel):
         gr.update(visible=True, value=error_fig),
     )
 
+
 # Gradio interface
 def create_gradio_ui():
     global glob_k, glob_a, glob_b, glob_c
-    
+
     # Get the initial available files
     with gr.Blocks() as demo:
         gr.Markdown("# Learn the Coefficients for the Heat Equation using the RFM")
@@ -467,7 +475,7 @@ def create_gradio_ui():
                 )
 
             plot_output = gr.Plot()
-            
+
         k_slider.change(
             fn=plot_function,
             inputs=[k_slider, a_slider, b_slider, c_slider],