Set npx to nh why not

app.py

@@ -16,8 +16,8 @@ def compute_radon_fn(image,
     taxis = np.linspace(-nt/2, nt/2, nt)   # time axis (rows)
     haxis = np.linspace(-nh/2, nh/2, nh)   # spatial axis (columns)
     # Set detector axis (pxaxis) to cover the full image diagonal.
-    # L = int(np.ceil(np.sqrt(M**2 + N**2)))
-    npx = nt # why?
+    # npx = int(np.ceil(np.sqrt(nt**2 + nh**2)))
+    npx = nh # why?
     pxaxis = np.linspace(-npx/2, npx/2, npx)
 
     print(f"Shapes:\n taxis:{taxis.shape}, haxis:{haxis.shape}, pxaxis:{pxaxis.shape}")
@@ -30,9 +30,9 @@ def compute_radon_fn(image,
     # Compute the forward radon transform.
     radon_data_flat = R.dot(image_gray.flatten())
     # Deduce the number of projections from the operator shape.
-    nproj = R.shape[0] // L
+    nproj = R.shape[0] // npx
     # Reshape to (nproj, L) so each row corresponds to one projection.
-    radon_data = radon_data_flat.reshape((nproj, L))
+    radon_data = radon_data_flat.reshape((nproj, npx))
     
     # Transpose for display: p (detector coordinate) on x-axis, τ on y-axis.
     radon_display = radon_data.T
@@ -44,7 +44,7 @@ def compute_radon_fn(image,
          "radon_data": radon_data,     # shape: (nproj, L)
          "image_shape": image_gray.shape,
          "R": R,                       # the Radon2D operator
-         "L": L                        # detector length
+         "L": npx                      # detector length
     }
     return radon_display, state