Utilities

Utility functions and classes.

lydata.utils.get_github_auth(token: str | None = None, user: str | None = None, password: str | None = None) → Auth | None

Get the GitHub authentication object from arguments or environment variables.

lydata.utils.update_and_expand(left: DataFrame, right: DataFrame, **update_kwargs: Any) → DataFrame

Update left with values from right, also adding columns from right.

The added feature of this function over pandas’ update() is that it also adds columns that are present in right but not in left.

Any keyword arguments are also directly passed to the update().

>>> left = pd.DataFrame({"a": [1, 2, None], "b": [3, 4, 5]})
>>> right = pd.DataFrame({"a": [None, 3, 4], "c": [6, 7, 8]})
>>> update_and_expand(left, right)
     a  b  c
0  1.0  3  6
1  3.0  4  7
2  4.0  5  8

lydata.utils.replace(left: DataFrame, right: DataFrame) → DataFrame

Replace all columns in left with those from right.

lydata.utils.get_default_column_map_old() → _ColumnMap

Get the old default column map.

This map defines which short column names can be used to access columns in the DataFrames.

>>> from lydata import accessor, loader
>>> df = next(loader.load_datasets(
...     institution="usz",
...     repo_name="lycosystem/lydata.private",
...     ref="ab04379a36b6946306041d1d38ad7e97df8ee7ba",
... ))
>>> df.ly.surgery   
0      False
...
286    False
Name: (patient, #, neck_dissection), Length: 287, dtype: bool
>>> df.ly.smoke   
0       True
...
286     True
Name: (patient, #, nicotine_abuse), Length: 287, dtype: bool

lydata.utils.is_old(dataset: DataFrame) → bool

Check if the dataset uses the old column names.

lydata.utils.get_default_column_map_new() → _ColumnMap

Get the old default column map.

This map defines which short column names can be used to access columns in the DataFrames.

>>> from lydata import accessor, loader
>>> df = next(loader.load_datasets(
...     institution="usz",
...     repo_name="lycosystem/lydata.private",
...     ref="fb55afa26ff78afa78274a86b131fb3014d0ceea",
... ))
>>> df.ly.surgery   
0      False
...
286    False
Name: (patient, core, neck_dissection), Length: 287, dtype: bool
>>> df.ly.smoke   
0       True
...
286     True
Name: (patient, core, nicotine_abuse), Length: 287, dtype: bool

class lydata.utils.ModalityConfig(*, spec: Annotated[float, Ge(ge=0.5), Le(le=1.0)], sens: Annotated[float, Ge(ge=0.5), Le(le=1.0)], kind: Literal['clinical', 'pathological'] = 'clinical')

Define a diagnostic or pathological modality.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

lydata.utils.get_default_modalities() → dict[str, ModalityConfig]

Get defaults values for sensitivities and specificities of modalities.

Taken from de Bondt et al. (2007) and Kyzas et al. (2008).