deepmd.utils.path

deepmd.utils.path#

Classes#

`DPPath`	The path class to data system (DeepmdData).
`DPOSPath`	The OS path class to data system (DeepmdData) for real directories.
`DPH5Path`	The path class to data system (DeepmdData) for HDF5 files.

Module Contents#

class deepmd.utils.path.DPPath[source]#

Bases: abc.ABC

The path class to data system (DeepmdData).

Parameters:

pathstr: path
modestr, optional: mode, by default “r”

abstract load_numpy() → numpy.ndarray[source]#

Load NumPy array.

Returns:

np.ndarray: loaded NumPy array

abstract load_txt(**kwargs) → numpy.ndarray[source]#

Load NumPy array from text.

Returns:

np.ndarray: loaded NumPy array

abstract save_numpy(arr: numpy.ndarray) → None[source]#

Save NumPy array.

Parameters:

arrnp.ndarray: NumPy array

abstract glob(pattern: str) → list[DPPath][source]#

Search path using the glob pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

abstract rglob(pattern: str) → list[DPPath][source]#

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

abstract is_file() → bool[source]#: Check if self is file.

abstract is_dir() → bool[source]#: Check if self is directory.

abstract __truediv__(key: str) → DPPath[source]#: Used for / operator.

abstract __lt__(other: DPPath) → bool[source]#: Whether this DPPath is less than other for sorting.

abstract __str__() → str[source]#: Represent string.

__repr__() → str[source]#

__eq__(other) → bool[source]#

__hash__()[source]#

property name: str[source]#

Abstractmethod:

Name of the path.

abstract mkdir(parents: bool = False, exist_ok: bool = False) → None[source]#

Make directory.

Parameters:

parentsbool, optional: If true, any missing parents of this directory are created as well.
exist_okbool, optional: If true, no error will be raised if the target directory already exists.

class deepmd.utils.path.DPOSPath(path: str, mode: str = 'r')[source]#

Bases: DPPath

The OS path class to data system (DeepmdData) for real directories.

Parameters:

pathstr: path
modestr, optional: mode, by default “r”

mode[source]#

load_numpy() → numpy.ndarray[source]#

Load NumPy array.

Returns:

np.ndarray: loaded NumPy array

load_txt(**kwargs) → numpy.ndarray[source]#

Load NumPy array from text.

Returns:

np.ndarray: loaded NumPy array

save_numpy(arr: numpy.ndarray) → None[source]#

Save NumPy array.

Parameters:

arrnp.ndarray: NumPy array

glob(pattern: str) → list[DPPath][source]#

Search path using the glob pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

rglob(pattern: str) → list[DPPath][source]#

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

is_file() → bool[source]#: Check if self is file.

is_dir() → bool[source]#: Check if self is directory.

__truediv__(key: str) → DPPath[source]#: Used for / operator.

__lt__(other: DPOSPath) → bool[source]#: Whether this DPPath is less than other for sorting.

__str__() → str[source]#: Represent string.

property name: str[source]#: Name of the path.

mkdir(parents: bool = False, exist_ok: bool = False) → None[source]#

Make directory.

Parameters:

parentsbool, optional: If true, any missing parents of this directory are created as well.
exist_okbool, optional: If true, no error will be raised if the target directory already exists.

class deepmd.utils.path.DPH5Path(path: str, mode: str = 'r')[source]#

Bases: DPPath

The path class to data system (DeepmdData) for HDF5 files.

Parameters:

pathstr: path
modestr, optional: mode, by default “r”

Notes

OS - HDF5 relationship:: directory - Group file - Dataset

mode[source]#

root_path[source]#

root[source]#

_name[source]#

classmethod _load_h5py(path: str, mode: str = 'r') → h5py.File[source]#

Load hdf5 file.

Parameters:

pathstr: path to hdf5 file
modestr, optional: mode, by default ‘r’

load_numpy() → numpy.ndarray[source]#

Load NumPy array.

Returns:

np.ndarray: loaded NumPy array

load_txt(dtype: numpy.dtype | None = None, **kwargs) → numpy.ndarray[source]#

Load NumPy array from text.

Returns:

np.ndarray: loaded NumPy array

save_numpy(arr: numpy.ndarray) → None[source]#

Save NumPy array.

Parameters:

arrnp.ndarray: NumPy array

glob(pattern: str) → list[DPPath][source]#

Search path using the glob pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

rglob(pattern: str) → list[DPPath][source]#

This is like calling DPPath.glob() with **/ added in front of the given relative pattern.

Parameters:

patternstr: glob pattern

Returns:

list[DPPath]: list of paths

property _keys: list[str][source]#: Walk all groups and dataset.

__file_new_keys: ClassVar[dict[h5py.File, list[str]]][source]#

property _new_keys[source]#: New keys that haven’t been cached.

classmethod _file_keys(file: h5py.File) → list[str][source]#: Walk all groups and dataset.

is_file() → bool[source]#: Check if self is file.

is_dir() → bool[source]#: Check if self is directory.

__truediv__(key: str) → DPPath[source]#: Used for / operator.

_connect_path(path: str) → str[source]#: Connect self with path.

__lt__(other: DPH5Path) → bool[source]#: Whether this DPPath is less than other for sorting.

__str__() → str[source]#: Returns path of self.

property name: str[source]#: Name of the path.

mkdir(parents: bool = False, exist_ok: bool = False) → None[source]#

Make directory.

Parameters:

parentsbool, optional: If true, any missing parents of this directory are created as well.
exist_okbool, optional: If true, no error will be raised if the target directory already exists.