Simulation#
- class scdiffeq.tools._simulation.Simulation(use_key: str = 'X_pca', time_key: str = 'Time point', N: int = 1, device: device | None = device(type='cpu'), *args, **kwargs)[source]#
Bases:
ABCParseSampled trajectories from an scDiffEq model
- Parameters:
- Returns:
None
- __init__(use_key: str = 'X_pca', time_key: str = 'Time point', N: int = 1, device: device | None = device(type='cpu'), *args, **kwargs)[source]#
- property idx#
- _to_adata_sim(Z_hat: ndarray) AnnData[source]#
- Parameters:
Z_hat (np.ndarray)
- Returns:
adata_sim (anndata.AnnData)
- __call__(diffeq, adata: AnnData, idx: Index, dt: float = 0.1, t: Tensor | None = None, *args, **kwargs) AnnData[source]#
Simulate trajectories by sampling from an scDiffEq model.
- Parameters:
() (diffeq) – lightning model.
adata (AnnData) – Input AnnDat object.
idx (pd.Index) – cell indices (corresponding to adata from which the model should initiate sampled trajectories.
- Returns:
adata_sim (anndata.AnnData)
- __parse__(kwargs: Dict, public: List | None = [None], private: List | None = [], ignore: List | None = []) None#
Made to be called during cls.__init__ of the inherited class. Central function of this autoparsing base class.
- Parameters:
kwargs (Dict,)
public (Optional[List] = [None],)
private (Optional[List] = [],)
ignore (Optional[List] = [])
- Return type:
None
- __update__(kwargs: Dict, public: List | None = [None], private: List | None = [], ignore: List | None = []) None#
To be called after __parse__ has already been called (e.g., during cls.__call__) of the inherited class.
- Parameters:
kwargs (Dict) – Typically, locals()
public (Optional[List] = [None])
private (Optional[List] = [])
ignore (Optional[List] = [])
class. (Second-most central function of this autoparsing base)
- Return type:
None