cellmap_flow.norm.input_normalize

Attributes

logger

Classes

`SerializableInterface`
`InputNormalizer`
`Dilate`
`EuclideanDistance`
`MinMaxNormalizer`
`LambdaNormalizer`
`ZScoreNormalizer`

Functions

`get_input_normalizers`(→ list[dict])
`deserialize_list`(→ list)
`get_normalizations`(→ list[InputNormalizer])

Module Contents

cellmap_flow.norm.input_normalize.logger

class cellmap_flow.norm.input_normalize.SerializableInterface

classmethod name()

process(data, **kwargs) → numpy.ndarray

to_dict()

property dtype

class cellmap_flow.norm.input_normalize.InputNormalizer

class cellmap_flow.norm.input_normalize.Dilate(size=1)

size = 1

class cellmap_flow.norm.input_normalize.EuclideanDistance(anisotropy=50, black_border=True, parallel=5, type='edt', activation='tanh')

anisotropy

black_border = True

parallel = 5

activation

property dtype

class cellmap_flow.norm.input_normalize.MinMaxNormalizer(min_value=0.0, max_value=255.0, invert=False)

min_value

max_value

property dtype

class cellmap_flow.norm.input_normalize.LambdaNormalizer(expression: str)

expression

property dtype

class cellmap_flow.norm.input_normalize.ZScoreNormalizer(mean=0.0, std=1.0)

mean

std

property dtype

normalize(data: numpy.ndarray) → numpy.ndarray

cellmap_flow.norm.input_normalize.get_input_normalizers() → list[dict]

cellmap_flow.norm.input_normalize.deserialize_list(elms: dict, T: type) → list

cellmap_flow.norm.input_normalize.get_normalizations(elms: dict) → list[InputNormalizer]