piscis#

Submodules#

Classes#

`Piscis`	Class for running the Piscis algorithm.
`PiscisLegacy`	Class for running the PyTorch port of the legacy Piscis algorithm originally implemented in JAX.

Package Contents#

class piscis.Piscis(model_name: str = '20251212', batch_size: int = 1, input_size: Tuple[int, int] | None = None, device: str | torch.device | None = None)#

Bases: _Piscis

Class for running the Piscis algorithm.

Attributes:

model_namestr: Model name.
batch_sizeint: Batch size for the CNN.
adjustmentstr: Adjustment type applied to images during preprocessing.
input_sizeTuple[int, int]: Input size for the CNN.
dilation_iterationsint: Number of iterations used to dilate ground truth labels during training.
channelsint: Number of channels in the input images.
deviceOptional[Union[str, torch.device]]: Device to run the model on.
poolingstr: Pooling type applied to labels.
modelSpotsModel: Model.

predict(x: numpy.ndarray | dask.array.Array, stack: bool = False, scale: float = 1.0, threshold: float = 0.5, min_distance: int = 1, intermediates: bool = False) → Tuple[numpy.ndarray, xarray.DataArray] | numpy.ndarray#

Predict spots in an image or stack of images.

Parameters:

xnp.ndarray or da.Array: Image or stack of images.
stackbool, optional: Whether x is a stack of images. Default is False.
scalefloat, optional: Scale factor for rescaling x. Default is 1.
thresholdfloat, optional: Spot detection threshold. Default is 0.5.
min_distanceint, optional: Minimum distance between spots. Default is 1.
intermediatesbool, optional: Whether to return intermediate feature maps. Default is False.

Returns:

coordsnp.ndarray: Predicted spot coordinates.
ynp.ndarray, optional: Intermediate feature maps. Only returned if intermediates is True.

class piscis.PiscisLegacy(model_name: str = '20230905', batch_size: int = 1, input_size: Tuple[int, int] | None = None, device: str | torch.device | None = None)#

Bases: _Piscis

Class for running the PyTorch port of the legacy Piscis algorithm originally implemented in JAX.

Attributes:

model_namestr: Model name.
batch_sizeint: Batch size for the CNN.
adjustmentstr: Adjustment type applied to images during preprocessing.
input_sizeTuple[int, int]: Input size for the CNN.
dilation_iterationsint: Number of iterations used to dilate ground truth labels during training.
channelsint: Number of channels in the input images.
deviceOptional[Union[str, torch.device]]: Device to run the model on.
poolingstr: Pooling type applied to labels.
modelSpotsModel: Model.

predict(x: numpy.ndarray | dask.array.Array, stack: bool = False, scale: float = 1.0, threshold: float = 1.0, min_distance: int = 1, intermediates: bool = False) → Tuple[numpy.ndarray, xarray.DataArray] | numpy.ndarray#

Predict spots in an image or stack of images.

Parameters:

xnp.ndarray or da.Array: Image or stack of images.
stackbool, optional: Whether x is a stack of images. Default is False.
scalefloat, optional: Scale factor for rescaling x. Default is 1.
thresholdfloat, optional: Spot detection threshold. Can be interpreted as the minimum number of fully confident pixels necessary to identify a spot. Default is 1.0.
min_distanceint, optional: Minimum distance between spots. Default is 1.
intermediatesbool, optional: Whether to return intermediate feature maps. Default is False.

Returns:

coordsnp.ndarray: Predicted spot coordinates.
ynp.ndarray, optional: Intermediate feature maps. Only returned if intermediates is True.