Perform the spline calculation in double precision

and make the spline precision 1e-8 to match rascaline

torch_spex/le.py

@@ -105,6 +105,5 @@ def laplacian_eigenstate_basis_derivative(index, r):
         laplacian_eigenstate_basis_derivative,
         np.sum(n_max_l),
         r_cut,
-        requested_accuracy=1e-6,
     )
 

torch_spex/physical_LE/physical_LE.py

@@ -114,7 +114,6 @@ def function_for_splining_index_derivative(index, r):
         function_for_splining_index_derivative,
         np.sum(n_max_l),
         a,
-        requested_accuracy=1e-6,
     )
     print("Number of spline points:", len(spliner.spline_positions))
 

torch_spex/splines.py

@@ -72,8 +72,9 @@ def __init__(
         self.start = start
         self.stop = stop
 
-        # initialize spline with 11 points
-        positions = torch.linspace(start, stop, 11)
+        # initialize spline with 11 points; the spline calculation
+        # is performed in double precision
+        positions = torch.linspace(start, stop, 11, dtype=torch.float64)
         self.register_buffer("spline_positions", positions)
         self.register_buffer("spline_values", values_fn(positions))
         self.register_buffer("spline_derivatives", derivatives_fn(positions))
@@ -91,7 +92,10 @@ def __init__(
 
             half_step = (self.spline_positions[1] - self.spline_positions[0]) / 2
             intermediate_positions = torch.linspace(
-                self.start + half_step, self.stop - half_step, n_intermediate_positions
+                self.start + half_step,
+                self.stop - half_step,
+                n_intermediate_positions,
+                dtype=torch.float64,
             )
 
             estimated_values = self.compute(intermediate_positions)