deepmd.tf.utils.data
Alias for backward compatibility.
Module Contents
Classes
Class for a data system. |
- class deepmd.tf.utils.data.DeepmdData(sys_path: str, set_prefix: str = 'set', shuffle_test: bool = True, type_map: List[str] | None = None, optional_type_map: bool = True, modifier=None, trn_all_set: bool = False, sort_atoms: bool = True)[source]
Class for a data system.
It loads data from hard disk, and mantains the data as a data_dict
- Parameters:
- sys_path
Path to the data system
- set_prefix
Prefix for the directories of different sets
- shuffle_test
If the test data are shuffled
- type_map
Gives the name of different atom types
- optional_type_map
If the type_map.raw in each system is optional
- modifier
Data modifier that has the method modify_data
- trn_all_set
Use all sets as training dataset. Otherwise, if the number of sets is more than 1, the last set is left for test.
- sort_atomsbool
Sort atoms by atom types. Required to enable when the data is directly feeded to descriptors except mixed types.
- add(key: str, ndof: int, atomic: bool = False, must: bool = False, high_prec: bool = False, type_sel: List[int] | None = None, repeat: int = 1, default: float = 0.0, dtype: numpy.dtype | None = None, output_natoms_for_type_sel: bool = False)[source]
Add a data item that to be loaded.
- Parameters:
- key
The key of the item. The corresponding data is stored in sys_path/set.*/key.npy
- ndof
The number of dof
- atomic
The item is an atomic property. If False, the size of the data should be nframes x ndof If True, the size of data should be nframes x natoms x ndof
- must
The data file sys_path/set.*/key.npy must exist. If must is False and the data file does not exist, the data_dict[find_key] is set to 0.0
- high_prec
Load the data and store in float64, otherwise in float32
- type_sel
Select certain type of atoms
- repeat
The data will be repeated repeat times.
- default
float
, default=0. default value of data
- dtype
np.dtype
,optional
the dtype of data, overwrites high_prec if provided
- output_natoms_for_type_selbool,
optional
if True and type_sel is True, the atomic dimension will be natoms instead of nsel
- reduce(key_out: str, key_in: str)[source]
Generate a new item from the reduction of another atom.
- Parameters:
- key_out
The name of the reduced item
- key_in
The name of the data item to be reduced
- check_batch_size(batch_size)[source]
Check if the system can get a batch of data with batch_size frames.
- check_test_size(test_size)[source]
Check if the system can get a test dataset with test_size frames.
- get_item_torch(index: int) dict [source]
Get a single frame data . The frame is picked from the data system by index. The index is coded across all the sets.
- Parameters:
- index
index of the frame
- get_batch(batch_size: int) dict [source]
Get a batch of data with batch_size frames. The frames are randomly picked from the data system.
- Parameters:
- batch_size
size of the batch
- get_test(ntests: int = -1) dict [source]
Get the test data with ntests frames.
- Parameters:
- ntests
Size of the test data set. If ntests is -1, all test data will be get.
- get_natoms_vec(ntypes: int)[source]
Get number of atoms and number of atoms in different types.
- Parameters:
- ntypes
Number of types (may be larger than the actual number of types in the system).
- Returns:
natoms
natoms[0]: number of local atoms natoms[1]: total number of atoms held by this processor natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
- _load_batch_set(set_name: deepmd.utils.path.DPPath)[source]
- _load_test_set(set_name: deepmd.utils.path.DPPath, shuffle_test)[source]
- _get_nframes(set_name: deepmd.utils.path.DPPath)[source]
- reformat_data_torch(data)[source]
Modify the data format for the requirements of Torch backend.
- Parameters:
- data
original data
- _load_set(set_name: deepmd.utils.path.DPPath)[source]
- _load_data(set_name, key, nframes, ndof_, atomic=False, must=True, repeat=1, high_prec=False, type_sel=None, default: float = 0.0, dtype: numpy.dtype | None = None, output_natoms_for_type_sel: bool = False)[source]
- _load_type(sys_path: deepmd.utils.path.DPPath)[source]
- _load_type_mix(set_name: deepmd.utils.path.DPPath)[source]
- _load_type_map(sys_path: deepmd.utils.path.DPPath)[source]
- _check_pbc(sys_path: deepmd.utils.path.DPPath)[source]
- _check_mode(set_path: deepmd.utils.path.DPPath)[source]