whitespace

python/lammps/core.py

@@ -1622,8 +1622,8 @@ class lammps(object):
     """Return a string with detailed information about any devices that are
     usable by the GPU package.
 
-    This is a wrapper around the :cpp:func:`lammps_get_gpu_device_info` 
-    function of the C-library interface. 
+    This is a wrapper around the :cpp:func:`lammps_get_gpu_device_info`
+    function of the C-library interface.
 
     :return: GPU device info string
     :rtype:  string

python/lammps/mliap/loader.py

@@ -29,7 +29,7 @@ from ctypes import pythonapi, c_int, c_void_p, py_object
 class DynamicLoader(importlib.abc.Loader):
     def __init__(self,module_name,library,api_version=1013):
         self.api_version = api_version
-        
+
         attr = "PyInit_"+module_name
         initfunc = getattr(library,attr)
         # c_void_p is standin for PyModuleDef *
@@ -44,7 +44,7 @@ class DynamicLoader(importlib.abc.Loader):
         createfunc.restype = py_object
         module = createfunc(self.module_def, spec, self.api_version)
         return module
-    
+
     def exec_module(self, module):
         execfunc = pythonapi.PyModule_ExecDef
         # c_void_p is standin for PyModuleDef *
@@ -59,12 +59,12 @@ def activate_mliappy(lmp):
         library = lmp.lib
         module_names = ["mliap_model_python_couple", "mliap_unified_couple"]
         api_version = library.lammps_python_api_version()
-        
+
         for module_name in module_names:
             # Make Machinery
             loader = DynamicLoader(module_name,library,api_version)
             spec = importlib.util.spec_from_loader(module_name,loader)
-            
+
             # Do the import
             module = importlib.util.module_from_spec(spec)
             sys.modules[module_name] = module

python/lammps/mliap/mliap_unified_lj.py

@@ -19,16 +19,16 @@ class MLIAPUnifiedLJ(MLIAPUnified):
 
     def compute_gradients(self, data):
         """Test compute_gradients."""
-    
+
     def compute_descriptors(self, data):
         """Test compute_descriptors."""
-    
+
     def compute_forces(self, data):
         """Test compute_forces."""
         eij, fij = self.compute_pair_ef(data)
         data.update_pair_energy(eij)
         data.update_pair_forces(fij)
- 
+
     def compute_pair_ef(self, data):
         rij = data.rij
 

python/lammps/mliap/pytorch.py

@@ -80,10 +80,10 @@ class TorchWrapper(torch.nn.Module):
 
             n_params : torch.nn.Module (None)
                 Number of NN model parameters
-            
+
             device : torch.nn.Module (None)
                 Accelerator device
-            
+
             dtype : torch.dtype (torch.float64)
                 Dtype to use on device
         """
@@ -325,6 +325,6 @@ class ElemwiseModels(torch.nn.Module):
         per_atom_attributes = torch.zeros(elems.size(dim=0), dtype=self.dtype)
         given_elems, elem_indices = torch.unique(elems, return_inverse=True)
         for i, elem in enumerate(given_elems):
-            self.subnets[elem].to(self.dtype) 
+            self.subnets[elem].to(self.dtype)
             per_atom_attributes[elem_indices == i] = self.subnets[elem](descriptors[elem_indices == i]).flatten()
         return per_atom_attributes