docs: fix word spellings

Signed-off-by: Jinzhe Zeng <[email protected]>

backend/read_env.py

@@ -43,7 +43,7 @@ def get_argument_from_env() -> tuple[str, list, list, dict, str, str]:
     """
     cmake_args = []
     extra_scripts = {}
-    # get variant option from the environment varibles, available: cpu, cuda, rocm
+    # get variant option from the environment variables, available: cpu, cuda, rocm
     dp_variant = os.environ.get("DP_VARIANT", "cpu").lower()
     if dp_variant == "cpu" or dp_variant == "":
         cmake_minimum_required_version = "3.16"

deepmd/__init__.py

@@ -17,7 +17,7 @@
 
 
 def DeepPotential(*args, **kwargs):
-    """Factory function that forwards to DeepEval (for compatbility
+    """Factory function that forwards to DeepEval (for compatibility
     and performance).
 
     Parameters

deepmd/backend/suffix.py

@@ -23,7 +23,7 @@ def format_model_suffix(
     """Check and format the suffixes of a filename.
 
     When preferred_backend is not given, this method checks the suffix of the filename
-    is within the suffixes of the any backends (with the given feature) and doesn't do formating.
+    is within the suffixes of the any backends (with the given feature) and doesn't do formatting.
     When preferred_backend is given, strict_prefer must be given.
     If strict_prefer is True and the suffix is not within the suffixes of the preferred backend,
     or strict_prefer is False and the suffix is not within the suffixes of the any backend with the given feature,

deepmd/calculator.py

@@ -32,7 +32,7 @@
 class DP(Calculator):
     """Implementation of ASE deepmd calculator.
 
-    Implemented propertie are `energy`, `forces` and `stress`
+    Implemented properties are `energy`, `forces` and `stress`
 
     Parameters
     ----------

deepmd/common.py

@@ -77,7 +77,7 @@ def select_idx_map(atom_types: np.ndarray, select_types: np.ndarray) -> np.ndarr
     Parameters
     ----------
     atom_types : np.ndarray
-        array specifing type for each atoms as integer
+        array specifying type for each atoms as integer
     select_types : np.ndarray
         types of atoms you want to find indices for
 
@@ -126,7 +126,7 @@ def make_default_mesh(pbc: bool, mixed_type: bool) -> np.ndarray:
 def j_deprecated(
     jdata: dict[str, "_DICT_VAL"], key: str, deprecated_key: list[str] = []
 ) -> "_DICT_VAL":
-    """Assert that supplied dictionary conaines specified key.
+    """Assert that supplied dictionary contains specified key.
 
     Parameters
     ----------
@@ -218,7 +218,7 @@ def get_np_precision(precision: "_PRECISION") -> np.dtype:
     Returns
     -------
     np.dtype
-        numpy presicion constant
+        numpy precision constant
 
     Raises
     ------

deepmd/dpmodel/atomic_model/base_atomic_model.py

@@ -158,7 +158,7 @@ def forward_common_atomic(
         Parameters
         ----------
         extended_coord
-            extended coodinates, shape: nf x (nall x 3)
+            extended coordinates, shape: nf x (nall x 3)
         extended_atype
             extended atom typs, shape: nf x nall
             for a type < 0 indicating the atomic is virtual.

deepmd/dpmodel/atomic_model/dp_atomic_model.py

@@ -100,7 +100,7 @@ def forward_atomic(
         Parameters
         ----------
         extended_coord
-            coodinates in extended region
+            coordinates in extended region
         extended_atype
             atomic type in extended region
         nlist
@@ -169,7 +169,7 @@ def serialize(self) -> dict:
         )
         return dd
 
-    # for subclass overriden
+    # for subclass overridden
     base_descriptor_cls = BaseDescriptor
     """The base descriptor class."""
     base_fitting_cls = BaseFitting

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -162,7 +162,7 @@ def forward_atomic(
         Parameters
         ----------
         extended_coord
-            coodinates in extended region, (nframes, nall * 3)
+            coordinates in extended region, (nframes, nall * 3)
         extended_atype
             atomic type in extended region, (nframes, nall)
         nlist
@@ -341,7 +341,7 @@ class DPZBLLinearEnergyAtomicModel(LinearEnergyAtomicModel):
         Mapping atom type to the name (str) of the type.
         For example `type_map[1]` gives the name of the type 1.
     smin_alpha
-        The short-range tabulated interaction will be swithed according to the distance of the nearest neighbor.
+        The short-range tabulated interaction will be switched according to the distance of the nearest neighbor.
         This distance is calculated by softmin.
     """
 

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -152,7 +152,7 @@ def make_atom_mask(
             self,
             atype: t_tensor,
         ) -> t_tensor:
-            """The atoms with type < 0 are treated as virutal atoms,
+            """The atoms with type < 0 are treated as virtual atoms,
             which serves as place-holders for multi-frame calculations
             with different number of atoms in different frames.
 
@@ -164,7 +164,7 @@ def make_atom_mask(
             Returns
             -------
             mask
-                True for real atoms and False for virutal atoms.
+                True for real atoms and False for virtual atoms.
 
             """
             # supposed to be supported by all backends

deepmd/dpmodel/common.py

@@ -30,7 +30,7 @@
     "int64": np.int64,
     "bool": bool,
     "default": GLOBAL_NP_FLOAT_PRECISION,
-    # NumPy doesn't have bfloat16 (and does't plan to add)
+    # NumPy doesn't have bfloat16 (and doesn't plan to add)
     # ml_dtypes is a solution, but it seems not supporting np.save/np.load
     # hdf5 hasn't supported bfloat16 as well (see https://forum.hdfgroup.org/t/11975)
     "bfloat16": ml_dtypes.bfloat16,

deepmd/dpmodel/descriptor/descriptor.py

@@ -110,7 +110,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 

deepmd/dpmodel/descriptor/dpa1.py

@@ -358,11 +358,11 @@ def get_dim_emb(self) -> int:
         return self.se_atten.dim_emb
 
     def mixed_types(self) -> bool:
-        """If true, the discriptor
+        """If true, the descriptor
         1. assumes total number of atoms aligned across frames;
         2. requires a neighbor list that does not distinguish different atomic types.
 
-        If false, the discriptor
+        If false, the descriptor
         1. assumes total number of atoms of each atom type aligned across frames;
         2. requires a neighbor list that distinguishes different atomic types.
 
@@ -385,7 +385,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 
@@ -459,7 +459,7 @@ def call(
         nlist
             The neighbor list. shape: nf x nloc x nnei
         mapping
-            The index mapping from extended to lcoal region. not used by this descriptor.
+            The index mapping from extended to local region. not used by this descriptor.
 
         Returns
         -------
@@ -602,7 +602,7 @@ def update_sel(
         Parameters
         ----------
         train_data : DeepmdDataSystem
-            data used to do neighbor statictics
+            data used to do neighbor statistics
         type_map : list[str], optional
             The name of each type of atoms
         local_jdata : dict
@@ -793,11 +793,11 @@ def __getitem__(self, key):
             raise KeyError(key)
 
     def mixed_types(self) -> bool:
-        """If true, the discriptor
+        """If true, the descriptor
         1. assumes total number of atoms aligned across frames;
         2. requires a neighbor list that does not distinguish different atomic types.
 
-        If false, the discriptor
+        If false, the descriptor
         1. assumes total number of atoms of each atom type aligned across frames;
         2. requires a neighbor list that distinguishes different atomic types.
 

deepmd/dpmodel/descriptor/dpa2.py

@@ -624,11 +624,11 @@ def get_dim_emb(self) -> int:
         return self.repformers.dim_emb
 
     def mixed_types(self) -> bool:
-        """If true, the discriptor
+        """If true, the descriptor
         1. assumes total number of atoms aligned across frames;
         2. requires a neighbor list that does not distinguish different atomic types.
 
-        If false, the discriptor
+        If false, the descriptor
         1. assumes total number of atoms of each atom type aligned across frames;
         2. requires a neighbor list that distinguishes different atomic types.
 
@@ -653,7 +653,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 
@@ -1021,7 +1021,7 @@ def update_sel(
         Parameters
         ----------
         train_data : DeepmdDataSystem
-            data used to do neighbor statictics
+            data used to do neighbor statistics
         type_map : list[str], optional
             The name of each type of atoms
         local_jdata : dict

deepmd/dpmodel/descriptor/hybrid.py

@@ -63,7 +63,7 @@ def __init__(
         for ii in range(1, self.numb_descrpt):
             assert (
                 self.descrpt_list[ii].get_ntypes() == self.descrpt_list[0].get_ntypes()
-            ), f"number of atom types in {ii}th descrptor {self.descrpt_list[0].__class__.__name__} does not match others"
+            ), f"number of atom types in {ii}th descriptor {self.descrpt_list[0].__class__.__name__} does not match others"
         # if hybrid sel is larger than sub sel, the nlist needs to be cut for each type
         hybrid_sel = self.get_sel()
         self.nlist_cut_idx: list[np.ndarray] = []
@@ -161,7 +161,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 
@@ -284,7 +284,7 @@ def update_sel(
         Parameters
         ----------
         train_data : DeepmdDataSystem
-            data used to do neighbor statictics
+            data used to do neighbor statistics
         type_map : list[str], optional
             The name of each type of atoms
         local_jdata : dict

deepmd/dpmodel/descriptor/make_base_descriptor.py

@@ -116,7 +116,7 @@ def share_params(self, base_class, shared_level, resume=False):
             """
             Share the parameters of self to the base_class with shared_level during multitask training.
             If not start from checkpoint (resume is False),
-            some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+            some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
             """
             pass
 
@@ -194,7 +194,7 @@ def update_sel(
             Parameters
             ----------
             train_data : DeepmdDataSystem
-                data used to do neighbor statictics
+                data used to do neighbor statistics
             type_map : list[str], optional
                 The name of each type of atoms
             local_jdata : dict

deepmd/dpmodel/descriptor/repformers.py

@@ -307,11 +307,11 @@ def __getitem__(self, key):
             raise KeyError(key)
 
     def mixed_types(self) -> bool:
-        """If true, the discriptor
+        """If true, the descriptor
         1. assumes total number of atoms aligned across frames;
         2. requires a neighbor list that does not distinguish different atomic types.
 
-        If false, the discriptor
+        If false, the descriptor
         1. assumes total number of atoms of each atom type aligned across frames;
         2. requires a neighbor list that distinguishes different atomic types.
 
@@ -1480,7 +1480,7 @@ def call(
         """
         Parameters
         ----------
-        g1_ext : nf x nall x ng1         extended single-atom chanel
+        g1_ext : nf x nall x ng1         extended single-atom channel
         g2 : nf x nloc x nnei x ng2  pair-atom channel, invariant
         h2 : nf x nloc x nnei x 3    pair-atom channel, equivariant
         nlist : nf x nloc x nnei        neighbor list (padded neis are set to 0)
@@ -1489,7 +1489,7 @@ def call(
 
         Returns
         -------
-        g1:     nf x nloc x ng1         updated single-atom chanel
+        g1:     nf x nloc x ng1         updated single-atom channel
         g2:     nf x nloc x nnei x ng2  updated pair-atom channel, invariant
         h2:     nf x nloc x nnei x 3    updated pair-atom channel, equivariant
         """

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -281,7 +281,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 
@@ -359,7 +359,7 @@ def call(
         nlist
             The neighbor list. shape: nf x nloc x nnei
         mapping
-            The index mapping from extended to lcoal region. not used by this descriptor.
+            The index mapping from extended to local region. not used by this descriptor.
 
         Returns
         -------
@@ -486,7 +486,7 @@ def update_sel(
         Parameters
         ----------
         train_data : DeepmdDataSystem
-            data used to do neighbor statictics
+            data used to do neighbor statistics
         type_map : list[str], optional
             The name of each type of atoms
         local_jdata : dict
@@ -525,7 +525,7 @@ def call(
         nlist
             The neighbor list. shape: nf x nloc x nnei
         mapping
-            The index mapping from extended to lcoal region. not used by this descriptor.
+            The index mapping from extended to local region. not used by this descriptor.
 
         Returns
         -------

deepmd/dpmodel/descriptor/se_r.py

@@ -46,7 +46,7 @@
 @BaseDescriptor.register("se_e2_r")
 @BaseDescriptor.register("se_r")
 class DescrptSeR(NativeOP, BaseDescriptor):
-    r"""DeepPot-SE_R constructed from only the radial imformation of atomic configurations.
+    r"""DeepPot-SE_R constructed from only the radial information of atomic configurations.
 
 
     Parameters
@@ -233,7 +233,7 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         Share the parameters of self to the base_class with shared_level during multitask training.
         If not start from checkpoint (resume is False),
-        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        some separated parameters (e.g. mean and stddev) will be re-calculated across different classes.
         """
         raise NotImplementedError
 
@@ -303,7 +303,7 @@ def call(
         nlist
             The neighbor list. shape: nf x nloc x nnei
         mapping
-            The index mapping from extended to lcoal region. not used by this descriptor.
+            The index mapping from extended to local region. not used by this descriptor.
 
         Returns
         -------
@@ -404,7 +404,7 @@ def update_sel(
         Parameters
         ----------
         train_data : DeepmdDataSystem
-            data used to do neighbor statictics
+            data used to do neighbor statistics
         type_map : list[str], optional
             The name of each type of atoms
         local_jdata : dict