move parameters docstring from constructor method to class itself (#1011

)

Otherwise it won't be shown in the document.

Also contains other cleanups.

deepmd/descriptor/hybrid.py

@@ -21,16 +21,18 @@
 from .loc_frame import DescrptLocFrame
 
 class DescrptHybrid ():
+    """Concate a list of descriptors to form a new descriptor.
+
+    Parameters
+    ----------
+    descrpt_list : list
+            Build a descriptor from the concatenation of the list of descriptors.
+    """
     def __init__ (self, 
                   descrpt_list : list
     ) -> None :
         """
         Constructor
-
-        Parameters
-        ----------
-        descrpt_list : list
-                Build a descriptor from the concatenation of the list of descriptors.
         """
         if descrpt_list == [] or descrpt_list is None:
             raise RuntimeError('cannot build descriptor from an empty list of descriptors.')
@@ -72,11 +74,12 @@ def get_dim_out (self) -> int:
     def get_nlist_i(self, 
                     ii : int
     ) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
-        """
+        """Get the neighbor information of the ii-th descriptor
+
         Parameters
         ----------
         ii : int
-                Get the neighbor information of the ii-th descriptor
+                The index of the descriptor
 
         Returns
         -------

deepmd/descriptor/loc_frame.py

@@ -9,35 +9,39 @@
 from deepmd.utils.sess import run_sess
 
 class DescrptLocFrame () :
+    """Defines a local frame at each atom, and the compute the descriptor as local
+    coordinates under this frame.
+
+    Parameters
+    ----------
+    rcut
+            The cut-off radius
+    sel_a : list[str]
+            The length of the list should be the same as the number of atom types in the system. 
+            `sel_a[i]` gives the selected number of type-i neighbors. 
+            The full relative coordinates of the neighbors are used by the descriptor.
+    sel_r : list[str]
+            The length of the list should be the same as the number of atom types in the system. 
+            `sel_r[i]` gives the selected number of type-i neighbors. 
+            Only relative distance of the neighbors are used by the descriptor.
+            sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.        
+    axis_rule: list[int]
+            The length should be 6 times of the number of types. 
+            - axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
+            - axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.\n\n\
+            - axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.\n\n\
+            - axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
+            - axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.\n\n\
+            - axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.    
+    """
     def __init__(self, 
                  rcut: float,
                  sel_a : List[int],
                  sel_r : List[int],
                  axis_rule : List[int]
     ) -> None:
         """
-        Constructor
-
-        Parameters
-        rcut
-                The cut-off radius
-        sel_a : list[str]
-                The length of the list should be the same as the number of atom types in the system. 
-                `sel_a[i]` gives the selected number of type-i neighbors. 
-                The full relative coordinates of the neighbors are used by the descriptor.
-        sel_r : list[str]
-                The length of the list should be the same as the number of atom types in the system. 
-                `sel_r[i]` gives the selected number of type-i neighbors. 
-                Only relative distance of the neighbors are used by the descriptor.
-                sel_a[i] + sel_r[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius.        
-        axis_rule: list[int]
-                The length should be 6 times of the number of types. 
-                - axis_rule[i*6+0]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
-                - axis_rule[i*6+1]: type of the atom defining the first axis of type-i atom.\n\n\
-                - axis_rule[i*6+2]: index of the axis atom defining the first axis. Note that the neighbors with the same class and type are sorted according to their relative distance.\n\n\
-                - axis_rule[i*6+3]: class of the atom defining the first axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.\n\n\
-                - axis_rule[i*6+4]: type of the atom defining the second axis of type-i atom.\n\n\
-                - axis_rule[i*6+5]: class of the atom defining the second axis of type-i atom. 0 for neighbors with full coordinates and 1 for neighbors only with relative distance.        
+        Constructor    
         """
         # args = ClassArg()\
         #        .add('sel_a',    list,   must = True) \

deepmd/descriptor/se_a.py

@@ -16,6 +16,44 @@
 from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph
 
 class DescrptSeA ():
+    """DeepPot-SE constructed from all information (both angular and radial) of
+    atomic configurations.
+    
+    The embedding takes the distance between atoms as input.
+
+    Parameters
+    ----------
+    rcut
+            The cut-off radius
+    rcut_smth
+            From where the environment matrix should be smoothed
+    sel : list[str]
+            sel[i] specifies the maxmum number of type i atoms in the cut-off radius
+    neuron : list[int]
+            Number of neurons in each hidden layers of the embedding net
+    axis_neuron
+            Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
+    resnet_dt
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+    trainable
+            If the weights of embedding net are trainable.
+    seed
+            Random seed for initializing the network parameters.
+    type_one_side
+            Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
+    exclude_types : List[List[int]]
+            The excluded pairs of types which have no interaction with each other.
+            For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+    set_davg_zero
+            Set the shift of embedding net input to zero.
+    activation_function
+            The activation function in the embedding net. Supported options are {0}
+    precision
+            The precision of the embedding net parameters. Supported options are {1}
+    uniform_seed
+            Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
+    """
     @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   rcut: float,
@@ -35,39 +73,6 @@ def __init__ (self,
     ) -> None:
         """
         Constructor
-
-        Parameters
-        ----------
-        rcut
-                The cut-off radius
-        rcut_smth
-                From where the environment matrix should be smoothed
-        sel : list[str]
-                sel[i] specifies the maxmum number of type i atoms in the cut-off radius
-        neuron : list[int]
-                Number of neurons in each hidden layers of the embedding net
-        axis_neuron
-                Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
-        resnet_dt
-                Time-step `dt` in the resnet construction:
-                y = x + dt * \phi (Wx + b)
-        trainable
-                If the weights of embedding net are trainable.
-        seed
-                Random seed for initializing the network parameters.
-        type_one_side
-                Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
-        exclude_types : List[List[int]]
-                The excluded pairs of types which have no interaction with each other.
-                For example, `[[0, 1]]` means no interaction between type 0 and type 1.
-        set_davg_zero
-                Set the shift of embedding net input to zero.
-        activation_function
-                The activation function in the embedding net. Supported options are {0}
-        precision
-                The precision of the embedding net parameters. Supported options are {1}
-        uniform_seed
-                Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
         """
         self.sel_a = sel
         self.rcut_r = rcut

deepmd/descriptor/se_a_ebd.py

@@ -12,6 +12,45 @@
 from .se_a import DescrptSeA
 
 class DescrptSeAEbd (DescrptSeA):
+    """DeepPot-SE descriptor with type embedding approach.
+
+    Parameters
+    ----------
+    rcut
+            The cut-off radius
+    rcut_smth
+            From where the environment matrix should be smoothed
+    sel : list[str]
+            sel[i] specifies the maxmum number of type i atoms in the cut-off radius
+    neuron : list[int]
+            Number of neurons in each hidden layers of the embedding net
+    axis_neuron
+            Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
+    resnet_dt
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+    trainable
+            If the weights of embedding net are trainable.
+    seed
+            Random seed for initializing the network parameters.
+    type_one_side
+            Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
+    type_nchanl
+            Number of channels for type representation
+    type_nlayer
+            Number of hidden layers for the type embedding net (skip connected).
+    numb_aparam
+            Number of atomic parameters. If >0 it will be embedded with atom types.
+    set_davg_zero
+            Set the shift of embedding net input to zero.
+    activation_function
+            The activation function in the embedding net. Supported options are {0}
+    precision
+            The precision of the embedding net parameters. Supported options are {1}
+    exclude_types : List[List[int]]
+            The excluded pairs of types which have no interaction with each other.
+            For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+    """
     def __init__ (self, 
                   rcut: float,
                   rcut_smth: float,
@@ -32,43 +71,6 @@ def __init__ (self,
     ) -> None:
         """
         Constructor
-
-        Parameters
-        ----------
-        rcut
-                The cut-off radius
-        rcut_smth
-                From where the environment matrix should be smoothed
-        sel : list[str]
-                sel[i] specifies the maxmum number of type i atoms in the cut-off radius
-        neuron : list[int]
-                Number of neurons in each hidden layers of the embedding net
-        axis_neuron
-                Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
-        resnet_dt
-                Time-step `dt` in the resnet construction:
-                y = x + dt * \phi (Wx + b)
-        trainable
-                If the weights of embedding net are trainable.
-        seed
-                Random seed for initializing the network parameters.
-        type_one_side
-                Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
-        type_nchanl
-                Number of channels for type representation
-        type_nlayer
-                Number of hidden layers for the type embedding net (skip connected).
-        numb_aparam
-                Number of atomic parameters. If >0 it will be embedded with atom types.
-        set_davg_zero
-                Set the shift of embedding net input to zero.
-        activation_function
-                The activation function in the embedding net. Supported options are {0}
-        precision
-                The precision of the embedding net parameters. Supported options are {1}
-        exclude_types : List[List[int]]
-                The excluded pairs of types which have no interaction with each other.
-                For example, `[[0, 1]]` means no interaction between type 0 and type 1.
         """
         # args = ClassArg()\
         #        .add('type_nchanl',      int,    default = 4) \

deepmd/descriptor/se_a_ef.py

@@ -12,6 +12,41 @@
 from .se_a import DescrptSeA
 
 class DescrptSeAEf ():
+    """
+
+    Parameters
+    ----------
+    rcut
+            The cut-off radius
+    rcut_smth
+            From where the environment matrix should be smoothed
+    sel : list[str]
+            sel[i] specifies the maxmum number of type i atoms in the cut-off radius
+    neuron : list[int]
+            Number of neurons in each hidden layers of the embedding net
+    axis_neuron
+            Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
+    resnet_dt
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+    trainable
+            If the weights of embedding net are trainable.
+    seed
+            Random seed for initializing the network parameters.
+    type_one_side
+            Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
+    exclude_types : List[List[int]]
+            The excluded pairs of types which have no interaction with each other.
+            For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+    set_davg_zero
+            Set the shift of embedding net input to zero.
+    activation_function
+            The activation function in the embedding net. Supported options are {0}
+    precision
+            The precision of the embedding net parameters. Supported options are {1}
+    uniform_seed
+            Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
+    """
     @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__(self,
                  rcut: float,
@@ -31,39 +66,6 @@ def __init__(self,
     ) -> None:        
         """
         Constructor
-
-        Parameters
-        ----------
-        rcut
-                The cut-off radius
-        rcut_smth
-                From where the environment matrix should be smoothed
-        sel : list[str]
-                sel[i] specifies the maxmum number of type i atoms in the cut-off radius
-        neuron : list[int]
-                Number of neurons in each hidden layers of the embedding net
-        axis_neuron
-                Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
-        resnet_dt
-                Time-step `dt` in the resnet construction:
-                y = x + dt * \phi (Wx + b)
-        trainable
-                If the weights of embedding net are trainable.
-        seed
-                Random seed for initializing the network parameters.
-        type_one_side
-                Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
-        exclude_types : List[List[int]]
-                The excluded pairs of types which have no interaction with each other.
-                For example, `[[0, 1]]` means no interaction between type 0 and type 1.
-        set_davg_zero
-                Set the shift of embedding net input to zero.
-        activation_function
-                The activation function in the embedding net. Supported options are {0}
-        precision
-                The precision of the embedding net parameters. Supported options are {1}
-        uniform_seed
-                Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
         """
         self.descrpt_para = DescrptSeAEfLower(
             op_module.descrpt_se_a_ef_para,