phenotypic.correction.VisuShrinkCorrector

class phenotypic.correction.VisuShrinkCorrector(*, use_gat: bool = False, gat_gain: Annotated[float, Gt(gt=0)] = 1.0, gat_mu: Annotated[float, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = 0.0, gat_read_sigma: Annotated[float, Ge(ge=0)] = 0.0, gat_scale_factor: Annotated[float | None, Gt(gt=0)] = None, sigma: Annotated[float | None, TuneSpec(low=0.01, high=0.1, step=None, log=True, categories=None, tunable=True)] = None, wavelet: str = 'db2', mode: Literal['soft', 'hard'] = 'soft', wavelet_levels: Annotated[int | None, TuneSpec(low=2, high=6, step=None, log=False, categories=None, tunable=True)] = None, convert2ycbcr: bool = True, rescale_sigma: bool = True, clip: bool = True)

Bases: _GATSupportMixin, ImageCorrector

Denoise all image components using the near-minimax VisuShrink universal wavelet threshold.

Apply VisuShrink wavelet denoising to RGB (if present), grayscale, and detection matrix simultaneously, using a single global threshold derived from the estimated noise level and image size. All three image representations are updated in one pass, keeping them mutually consistent. The universal threshold is deliberately conservative and tends to over-smooth relative to adaptive alternatives.

For algorithm details, see What Enhancement Actually Does.

Best For:

• Quick, consistent denoising of raw plate scans before archival or publication where a single noise level characterizes the whole image.

• Plates with uniform scanner or camera noise where per-subband adaptation provides little additional benefit.

• Removing broad-field scanner banding or read noise from all image components before downstream multi-channel analysis.

Consider Also:

• BayesShrinkCorrector for adaptive per-subband thresholds that preserve finer colony detail at the cost of slightly more computation.

• DenoiseBlockMatch for GAT-stabilized BM3D collaborative filtering when Poisson-Gaussian noise modelling is important.

• VisuShrinkEnhancer when only the detection matrix should be denoised and RGB and gray must remain untouched.

Parameters:

• sigma (Annotated[float | None, TuneSpec(low=0.01, high=0.1, step=None, log=True, categories=None, tunable=True)]) – Noise standard deviation on the [0, 1] intensity scale. None auto-estimates from the median absolute deviation of the finest wavelet detail subband (MAD / 0.6745). The universal threshold T = sigma * sqrt(2 * log(N)) is conservative; reducing sigma by factors of 2–4 below the auto-estimate gives sharper output. Internally retargeted to 1.0 for gray and detect_mat when use_gat=True; RGB is not GAT-wrapped. Default: None.

• wavelet (str) – PyWavelets wavelet family name. 'db2' is general-purpose with compact support; 'db4' has more vanishing moments and wider support, representing smooth colony interiors more sparsely. Default: 'db2'.

• mode (Literal['soft', 'hard']) – Wavelet coefficient thresholding mode. 'soft' shrinks surviving coefficients toward zero, producing smoother output preferred for detection preprocessing. 'hard' zeroes coefficients below the threshold and leaves the rest unchanged, retaining sharper edges but introducing discontinuities that can appear as ringing near sharp boundaries. Default: 'soft'.

• wavelet_levels (Annotated[int | None, TuneSpec(low=2, high=6, step=None, log=False, categories=None, tunable=True)]) – Number of wavelet decomposition levels. None uses the library heuristic (maximum possible minus three). Practical manual range: 2–6. More levels suppress noise at coarser spatial scales; fewer levels restrict denoising to fine-grained noise. Default: None.

• convert2ycbcr (bool) – Convert RGB to YCbCr before denoising so luminance and chrominance channels are thresholded independently, preserving colony hue for pigmentation assays. Applies only when RGB data is present. Default: True.

• rescale_sigma (bool) – Allow skimage to internally rescale sigma when converting between dtypes. Automatically deferred to False during the gray and detect_mat passes when use_gat=True to prevent double-scaling the stabilized-domain noise level. Default: True.

• clip (bool) – Clamp gray and detect_mat outputs to [0, 1] after denoising. Soft thresholding can produce marginally negative values near dark colony edges; clipping eliminates these. Automatically deferred to False inside the GAT region so the inverse transform operates on the full stabilized signal before the final clamp. Default: True.

• parameters (# GAT)

• use_gat (bool) – Wrap gray and detect_mat denoising in the Generalized Anscombe Transform, converting Poisson-Gaussian noise into approximately unit-variance Gaussian noise so the VisuShrink threshold is correctly scaled across intensity levels. RGB is not transformed. Enable for fluorescence plate readers or low-light incubator images. Default: False.

• gat_gain (Annotated[float, Gt(gt=0)]) – Camera gain in electrons per ADU. Typical range: 0.5–10 e-/ADU depending on sensor. Obtain from the manufacturer datasheet or a mean-variance calibration. Default: 1.0.

• gat_mu (Annotated[float, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – Read-noise mean (dark-current baseline offset) in count units consistent with gat_scale_factor. Set to 0.0 when the image has been bias-subtracted, covering most flatbed-scanner workflows. Default: 0.0.

• gat_read_sigma (Annotated[float, Ge(ge=0)]) – Read-noise standard deviation in count units consistent with gat_scale_factor. Zero assumes pure Poisson noise; supplying the sensor read-noise spec (a few to a few tens of counts for typical scientific CCDs) improves stabilization at low signal levels. Default: 0.0.

• gat_scale_factor (Annotated[float | None, Gt(gt=0)]) – Multiplier converting normalized [0, 1] float data to photon counts before the forward GAT. None auto-detects from image bit depth (8-bit: 255, 16-bit: 65535). Override for non-standard bit depths such as 12-bit sensors stored in a 16-bit container (4095). Default: None.

Returns:

Input image with all components (rgb, gray, detect_mat) transformed by VisuShrink wavelet denoising. All three representations are updated in a single pass.

Return type:

Image

References

[1] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, Sep. 1994.

[2] M. Mäkitalo and A. Foi, “Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 91–103, Jan. 2013.

See also

How To: Denoise Low-Light Images for a visual walkthrough of wavelet denoising on plate images. Image Quality: Noise, Contrast, and Structure for background on noise models and denoising strategy selection.

Methods

__init__	Create a new model by parsing and validating input data from keyword arguments.
apply	Applies the operation to an image, either in-place or on a copy.
construct
copy	Returns a copy of the model.
dict
from_json	Reconstruct an operation from JSON written by to_json().
from_orm
json
model_construct	Creates a new instance of the Model class with validated data.
model_copy	!!! abstract "Usage Documentation"
model_dump	!!! abstract "Usage Documentation"
model_dump_json	!!! abstract "Usage Documentation"
model_json_schema	Generates a JSON schema for a model class.
model_parametrized_name	Compute the class name for parametrizations of generic classes.
model_post_init	Initialize logging and memory tracking after model construction.
model_rebuild	Try to rebuild the pydantic-core schema for the model.
model_validate	Validate a pydantic model instance.
model_validate_json	!!! abstract "Usage Documentation"
model_validate_strings	Validate the given object with string data against the Pydantic model.
parse_file
parse_obj
parse_raw
schema
schema_json
to_json	Serialize this operation to JSON.
update_forward_refs
validate
widget	Return (and optionally display) the root widget.

Attributes

model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
sigma
wavelet
mode
wavelet_levels
convert2ycbcr
rescale_sigma
clip
use_gat
gat_gain
gat_mu
gat_read_sigma
gat_scale_factor

sigma: Annotated[float | None, TuneSpec(0.01, 0.1, log=True)]

wavelet: str

mode: Literal['soft', 'hard']

wavelet_levels: Annotated[int | None, TuneSpec(2, 6)]

convert2ycbcr: bool

rescale_sigma: bool

clip: bool

__copy__() → Self

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo: dict[int, Any] | None = None) → Self

Returns a deep copy of the model.

Parameters:

memo (dict[int, Any] | None)

Return type:

Self

__del__()

Automatically stop tracemalloc when the object is deleted.

classmethod __get_pydantic_json_schema__(core_schema: CoreSchema, handler: GetJsonSchemaHandler, /) → JsonSchemaValue

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

Return type:

JsonSchemaValue

__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__() → Generator[tuple[str, Any], None, None]

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

Return type:

Generator[Any]

classmethod __pydantic_init_subclass__(**kwargs: Any) → None

Populate field descriptions from the subclass docstring.

Runs once per concrete subclass after pydantic has built its model. Copies parameter descriptions parsed from the Google-style Args: docstring block onto each field’s description slot so they surface in model_json_schema() — the machine-readable contract used by downstream tooling (e.g. an MCP server).

Parameters:

**kwargs (Any) – Class-keyword arguments forwarded by pydantic.

Return type:

None

classmethod __pydantic_on_complete__() → None

This is called once the class and its fields are fully initialized and ready to be used.

This typically happens when the class is created (just before [__pydantic_init_subclass__()][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), except when forward annotations are used that could not immediately be resolved. In that case, it will be called later, when the model is rebuilt automatically or explicitly using [model_rebuild()][pydantic.main.BaseModel.model_rebuild].

Return type:

None

__repr_name__() → str

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object: Any) → str

Returns the string representation of a recursive object.

Parameters:

object (Any)

Return type:

str

__rich_repr__() → RichReprResult

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

RichReprResult

apply(image: Image, inplace: bool = False) → Image

Applies the operation to an image, either in-place or on a copy.

Parameters:

• image (Image) – The arr image to apply the operation on.

• inplace (bool) – If True, modifies the image in place; otherwise, operates on a copy of the image.

Returns:

The modified image after applying the operation.

Return type:

Image

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

Return type:

Self

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Self

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.

• exclude (AbstractSetIntStr | MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.

• update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.

• deep (bool) – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

Return type:

Self

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

Parameters:

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

Return type:

Dict[str, Any]

classmethod from_json(json_data: str | Path | dict) → BaseOperation

Reconstruct an operation from JSON written by to_json().

Accepts a JSON string, a path to a JSON file, or a pre-parsed envelope dict (same input handling as ImagePipeline.from_json()). Polymorphic: ImageOperation.from_json(path) returns whatever concrete operation the file holds. When called on a narrower subclass, the resolved class must be a subclass of it, else a TypeError is raised.

Parameters:

json_data (str | Path | dict) – A JSON string, path to a JSON file, or envelope dict.

Returns:

The reconstructed operation instance.

Raises:

• AttributeError – If the recorded class cannot be resolved in the phenotypic namespace.

• TypeError – If called on a concrete subclass and the file holds a class that is not a subclass of it.

Return type:

BaseOperation

Example

>>> import tempfile
>>> from pathlib import Path
>>> from phenotypic.abc_ import ImageOperation
>>> from phenotypic.detect import OtsuDetector
>>> with tempfile.TemporaryDirectory() as d:
...     p = Path(d) / "op.json"
...     OtsuDetector().to_json(p)
...     loaded = ImageOperation.from_json(p)  # polymorphic
>>> type(loaded).__name__
'OtsuDetector'

classmethod from_orm(obj: Any) → Self

Parameters:

obj (Any)

Return type:

Self

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

Parameters:

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

Return type:

str

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'validate_assignment': True}

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

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Return type:

Self

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self

!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Returns:

New model instance.

Return type:

Self

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → dict[str, Any]

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to include in the output.

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → str

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii (bool) – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to include in the JSON output.

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• exclude_computed_fields (bool) – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (bool | Literal['none', 'warn', 'error']) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Callable[[Any], Any] | None) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Return type:

str

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'clip': FieldInfo(annotation=bool, required=False, default=True, description='Clamp gray and detect_mat outputs to [0, 1] after denoising. Soft thresholding can produce marginally negative values near dark colony edges; clipping eliminates these. Automatically deferred to ``False`` inside the GAT region so the inverse transform operates on the full stabilized signal before the final clamp. Default: ``True``. # GAT parameters (active only when use_gat=True)'), 'convert2ycbcr': FieldInfo(annotation=bool, required=False, default=True, description='Convert RGB to YCbCr before denoising so luminance and chrominance channels are thresholded independently, preserving colony hue for pigmentation assays. Applies only when RGB data is present. Default: ``True``.'), 'gat_gain': FieldInfo(annotation=float, required=False, default=1.0, description='Camera gain in electrons per ADU. Typical range: 0.5--10 e-/ADU depending on sensor. Obtain from the manufacturer datasheet or a mean-variance calibration. Default: ``1.0``.', metadata=[Gt(gt=0)]), 'gat_mu': FieldInfo(annotation=float, required=False, default=0.0, description='Read-noise mean (dark-current baseline offset) in count units consistent with ``gat_scale_factor``. Set to 0.0 when the image has been bias-subtracted, covering most flatbed-scanner workflows.', metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'gat_read_sigma': FieldInfo(annotation=float, required=False, default=0.0, description='Read-noise standard deviation in count units consistent with ``gat_scale_factor``. Zero assumes pure Poisson noise; supplying the sensor read-noise spec (a few to a few tens of counts for typical scientific CCDs) improves stabilization at low signal levels. Default: ``0.0``.', metadata=[Ge(ge=0)]), 'gat_scale_factor': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Multiplier converting normalized [0, 1] float data to photon counts before the forward GAT. ``None`` auto-detects from image bit depth (8-bit: 255, 16-bit: 65535). Override for non-standard bit depths such as 12-bit sensors stored in a 16-bit container (4095). Default: ``None``.', metadata=[Gt(gt=0)]), 'mode': FieldInfo(annotation=Literal['soft', 'hard'], required=False, default='soft', description="Wavelet coefficient thresholding mode. ``'soft'`` shrinks surviving coefficients toward zero, producing smoother output preferred for detection preprocessing. ``'hard'`` zeroes coefficients below the threshold and leaves the rest unchanged, retaining sharper edges but introducing discontinuities that can appear as ringing near sharp boundaries. Default: ``'soft'``."), 'rescale_sigma': FieldInfo(annotation=bool, required=False, default=True, description='Allow skimage to internally rescale sigma when converting between dtypes. Automatically deferred to ``False`` during the gray and detect_mat passes when ``use_gat=True`` to prevent double-scaling the stabilized-domain noise level. Default: ``True``.'), 'sigma': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Noise standard deviation on the [0, 1] intensity scale. ``None`` auto-estimates from the median absolute deviation of the finest wavelet detail subband (MAD / 0.6745). The universal threshold T = sigma * sqrt(2 * log(N)) is conservative; reducing sigma by factors of 2--4 below the auto-estimate gives sharper output. Internally retargeted to 1.0 for gray and detect_mat when ``use_gat=True``; RGB is not GAT-wrapped. Default: ``None``.', metadata=[TuneSpec(low=0.01, high=0.1, step=None, log=True, categories=None, tunable=True)]), 'use_gat': FieldInfo(annotation=bool, required=False, default=False, description='Wrap gray and detect_mat denoising in the Generalized Anscombe Transform, converting Poisson-Gaussian noise into approximately unit-variance Gaussian noise so the VisuShrink threshold is correctly scaled across intensity levels. RGB is not transformed. Enable for fluorescence plate readers or low-light incubator images. Default: ``False``.'), 'wavelet': FieldInfo(annotation=str, required=False, default='db2', description="PyWavelets wavelet family name. ``'db2'`` is general-purpose with compact support; ``'db4'`` has more vanishing moments and wider support, representing smooth colony interiors more sparsely."), 'wavelet_levels': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of wavelet decomposition levels. ``None`` uses the library heuristic (maximum possible minus three). Practical manual range: 2--6. More levels suppress noise at coarser spatial scales; fewer levels restrict denoising to fine-grained noise.', metadata=[TuneSpec(low=2, high=6, step=None, log=False, categories=None, tunable=True)])}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• union_format (Literal['any_of', 'primitive_type_array']) –

  The format to use when combining schemas from unions together. Can be one of:

  • ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

  keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

Return type:

str

model_post_init(_BaseOperation__context: Any) → None

Initialize logging and memory tracking after model construction.

Replaces the legacy __init__ body: creates the per-class logger and, when that logger is enabled for INFO level or higher, starts tracemalloc so per-operation memory usage can be logged.

Parameters:

• __context – Pydantic post-init context (unused).

• _BaseOperation__context (Any)

Return type:

None

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: MappingNamespace | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (MappingNamespace | None) – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

Return type:

bool | None

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

Self

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

Return type:

Self

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• extra (Literal['allow', 'ignore', 'forbid'] | None) – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Return type:

Self

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

Parameters:

• path (str | Path)

• content_type (str | None)

• encoding (str)

• proto (DeprecatedParseProtocol | None)

• allow_pickle (bool)

Return type:

Self

classmethod parse_obj(obj: Any) → Self

Parameters:

obj (Any)

Return type:

Self

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (DeprecatedParseProtocol | None)

• allow_pickle (bool)

Return type:

Self

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

Return type:

str

to_json(filepath: str | Path | None = None) → str | None

Serialize this operation to JSON.

Captures the operation as a {"class", "params"} envelope: params is model_dump(mode="json") (every declared field, including nested operations and raw arrays; PrivateAttr state such as loggers and timing is excluded automatically), and class records the concrete class name so from_json() can rebuild the right subclass. This mirrors ImagePipeline.to_json().

Parameters:

filepath (str | Path | None) – Optional path to write the JSON to. When None, the JSON string is returned instead. Accepts a str or Path.

Returns:

The JSON string when filepath is None, otherwise None.

Return type:

str | None

Example

>>> import tempfile
>>> from pathlib import Path
>>> from phenotypic.detect import OtsuDetector
>>> from phenotypic.sdk_ import CONFIG_SUFFIX_OPERATION, ensure_typed_json_suffix
>>> with tempfile.TemporaryDirectory() as d:
...     p = Path(d) / "op.json"
...     saved = ensure_typed_json_suffix(p, CONFIG_SUFFIX_OPERATION)
...     OtsuDetector(ignore_zeros=True).to_json(p)
...     loaded = OtsuDetector.from_json(saved)
>>> loaded.ignore_zeros
True

classmethod update_forward_refs(**localns: Any) → None

Parameters:

localns (Any)

Return type:

None

classmethod validate(value: Any) → Self

Parameters:

value (Any)

Return type:

Self

widget(image: Image | None = None, show: bool = False) → Widget

Return (and optionally display) the root widget.

Parameters:

• image (Image | None) – Optional image to visualize. If provided, visualization controls will be added to the widget.

• show (bool) – Whether to display the widget immediately. Defaults to False.

Returns:

The root widget.

Return type:

ipywidgets.Widget

Raises:

ImportError – If ipywidgets or IPython are not installed.

use_gat: bool

gat_gain: float

gat_mu: Annotated[float, TuneSpec(tunable=False)]

gat_read_sigma: float

gat_scale_factor: float | None