fix: Sphinx deploy

docs/requirements.txt

@@ -1,6 +1,6 @@
-furo==2021.11.16
+furo
 ipykernel
-nbsphinx==0.8.9
-sphinx-panels==0.6.0
-sphinx-rtd-theme==1.0.0
+nbsphinx
+sphinx-panels
+sphinx-rtd-theme
 torchlaplace

torchlaplace/inverse_laplace.py

@@ -30,12 +30,6 @@
   different numerical algorithms. Each class provides methods to compute the
   ILT and evaluate the Laplace representation at specific points.
 
-Example:
-  ```
-  decoder = FixedTablot(ilt_reconstruction_terms=33).to(device)
-  print(f"FixedTablot Loss: {np.sqrt(torch.nn.MSELoss()(ft(t),
-    f_hat_t).cpu().numpy())}")
-  ```
 """
 ###########################
 # Neural Laplace: Learning diverse classes of differential equations in the

torchlaplace/transformations.py

@@ -4,15 +4,15 @@
 
 Functions:
 - spherical_riemann_to_complex: Converts spherical Riemann stereographic
-  projection coordinates to complex number coordinates.
+projection coordinates to complex number coordinates.
 - complex_to_spherical_riemann: Converts complex number coordinates to
-  spherical Riemann stereographic projection coordinates.
+spherical Riemann stereographic projection coordinates.
 - spherical_to_complex: Converts spherical Riemann stereographic projection
-  coordinates to complex number coordinates, maintaining the shape of the input
-  tensor.
+coordinates to complex number coordinates, maintaining the shape of the input
+tensor.
 - complex_to_spherical: Converts complex number coordinates to spherical
-  Riemann stereographic projection coordinates, maintaining the shape of the
-  input tensor.
+Riemann stereographic projection coordinates, maintaining the shape of the
+input tensor.
 
 The transformations are based on the mathematical mappings between the Riemann
   sphere and the complex plane.