pyhealth.datasets.utils#

Several utility functions.

pyhealth.datasets.utils.hash_str(s)[source]#

pyhealth.datasets.utils.strptime(s)[source]#

Helper function which parses a string to datetime object.

Parameters: s (str) – str, string to be parsed.
Return type: Optional[datetime]
Returns: Optional[datetime], parsed datetime object. If s is nan, return None.

pyhealth.datasets.utils.flatten_list(l)[source]#

Flattens a list of list.

Parameters: l (List) – List, the list of list to be flattened.
Return type: List
Returns: List, the flattened list.

Examples

>>> flatten_list([[1], [2, 3], [4]])
[1, 2, 3, 4]R
>>> flatten_list([[1], [[2], 3], [4]])
[1, [2], 3, 4]

pyhealth.datasets.utils.list_nested_levels(l)[source]#

Gets all the different nested levels of a list.

Parameters: l (List) – the list to be checked.
Return type: Tuple[int]
Returns: All the different nested levels of the list.

Examples

>>> list_nested_levels([])
(1,)
>>> list_nested_levels([1, 2, 3])
(1,)
>>> list_nested_levels([[]])
(2,)
>>> list_nested_levels([[1, 2, 3], [4, 5, 6]])
(2,)
>>> list_nested_levels([1, [2, 3], 4])
(1, 2)
>>> list_nested_levels([[1, [2, 3], 4]])
(2, 3)

pyhealth.datasets.utils.is_homo_list(l)[source]#

Checks if a list is homogeneous.

Parameters: l (List) – the list to be checked.
Return type: bool
Returns: bool, True if the list is homogeneous, False otherwise.

Examples

>>> is_homo_list([1, 2, 3])
True
>>> is_homo_list([])
True
>>> is_homo_list([1, 2, "3"])
False
>>> is_homo_list([1, 2, 3, [4, 5, 6]])
False

pyhealth.datasets.utils.collate_fn_dict(batch)[source]#

pyhealth.datasets.utils.get_dataloader(dataset, batch_size, shuffle=False)[source]#