phenotypic.enhance.FocusEdgeMonogenicPhase#

class phenotypic.enhance.FocusEdgeMonogenicPhase(*, n_scale: Annotated[int, Ge(ge=2), TuneSpec(low=3, high=6, step=None, log=False, categories=None, tunable=True)] = 4, min_wavelength: Annotated[float, Ge(ge=2.0), TuneSpec(low=2.0, high=10.0, step=None, log=False, categories=None, tunable=True)] = 3.0, mult: Annotated[float, Gt(gt=1.0), TuneSpec(low=1.5, high=3.0, step=None, log=False, categories=None, tunable=True)] = 2.1, sigma_onf: Annotated[float, Ge(ge=0.1), Lt(lt=1.0), TuneSpec(low=0.1, high=0.99, step=None, log=False, categories=None, tunable=True)] = 0.55, k: Annotated[float, Ge(ge=0.0), TuneSpec(low=0.5, high=20.0, step=None, log=False, categories=None, tunable=True)] = 3.0, deviation_gain: Annotated[float, Gt(gt=0.0), TuneSpec(low=1.0, high=2.0, step=None, log=False, categories=None, tunable=True)] = 1.5, cutoff: Annotated[float, Gt(gt=0.0), Lt(lt=1.0), TuneSpec(low=0.3, high=0.7, step=None, log=False, categories=None, tunable=True)] = 0.5, g: Annotated[float, Gt(gt=0.0), TuneSpec(low=2.0, high=20.0, step=None, log=False, categories=None, tunable=True)] = 10.0, noise_method: Annotated[float, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)] = -1.0, output: Literal['pc', 'orientation', 'feature_type'] = 'pc', norm: Literal['clip', 'rescale'] | None = 'clip')[source]#

Bases: NormalizedOutputMixin, FocusEdge

Enhance colony edges in detect_mat using monogenic phase congruency.

Detects features where the log-Gabor Fourier components are maximally in phase, producing an edge response that depends on phase agreement rather than amplitude. The result is invariant to local illumination level and scanner vignetting, so faint or translucent colony boundaries stay visible where intensity-gradient methods fail.

Unlike FocusEdgePhase, which sweeps a bank of oriented filters, this uses the Riesz transform to obtain the two odd (quadrature) channels isotropically. Orientation falls out of that pair instead of being searched for, so there is no n_orient parameter and the filter bank is n_orient times smaller.

Best For:

Colony boundaries that vary in opacity or contrast across the plate
Filamentous edges where an oriented bank’s angular quantization blurs the response between two adjacent orientations
Plates where you want a cheaper, isotropic alternative to FocusEdgePhase

Parameters:

n_scale (Annotated[int, Ge(ge=2), TuneSpec(low=3, high=6, step=None, log=False, categories=None, tunable=True)]) – Number of log-Gabor scales. Must be at least 2 – the frequency-spread weight divides by n_scale - 1. More scales widen the frequency coverage at linear cost.
min_wavelength (Annotated[float, Ge(ge=2.0), TuneSpec(low=2.0, high=10.0, step=None, log=False, categories=None, tunable=True)]) – Wavelength of the finest scale, in pixels. Raise it to ignore fine texture such as agar speckle.
mult (Annotated[float, Gt(gt=1.0), TuneSpec(low=1.5, high=3.0, step=None, log=False, categories=None, tunable=True)]) – Wavelength multiplier between successive scales. 2.1 with sigma_onf=0.55 gives roughly two-octave filter bandwidths.
sigma_onf (Annotated[float, Ge(ge=0.1), Lt(lt=1.0), TuneSpec(low=0.1, high=0.99, step=None, log=False, categories=None, tunable=True)]) – Ratio of each filter’s Gaussian sigma to its centre frequency. Smaller means narrower bandwidth, more scales needed for coverage.
k (Annotated[float, Ge(ge=0.0), TuneSpec(low=0.5, high=20.0, step=None, log=False, categories=None, tunable=True)]) – Number of noise standard deviations above the mean at which the noise threshold sits. ``phasecongmono``’s default is 3.0, not FocusEdgePhase’s 2.0. Raise it on noisy scans.
deviation_gain (Annotated[float, Gt(gt=0.0), TuneSpec(low=1.0, high=2.0, step=None, log=False, categories=None, tunable=True)]) – Scales the phase-deviation term, sharpening edge localization. Kovesi: “sensible values are from 1 to about 2.” Above ~2 the response becomes very sparse.
cutoff (Annotated[float, Gt(gt=0.0), Lt(lt=1.0), TuneSpec(low=0.3, high=0.7, step=None, log=False, categories=None, tunable=True)]) – Fractional frequency-spread below which the response is penalized, so that a feature excited at a single scale scores lower than a broadband one.
g (Annotated[float, Gt(gt=0.0), TuneSpec(low=2.0, high=20.0, step=None, log=False, categories=None, tunable=True)]) – Sharpness of the frequency-spread sigmoid.
noise_method (Annotated[float, TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]) – -1 estimates the Rayleigh noise parameter from the median of the finest scale’s amplitude; -2 uses its histogram mode. Any value >= 0 is used verbatim as the threshold, so 0.0 disables it.
output (Literal['pc', 'orientation', 'feature_type']) –
Which map to write to detect_mat. "pc" is the congruency in [0, 1]. "orientation" and "feature_type" are angles in [-pi/2, pi/2], mapped to [0, 1] by (theta + pi/2)/pi, since detect_mat must lie in the unit interval; invert the map to recover radians. For "orientation", 0.5 is a vertical edge and 1.0 a horizontal one. For "feature_type", 0.5 is a step edge, 1.0 a bright line and 0.0 a dark line.

The two angle maps are diagnostic, not detectable. An angle is defined everywhere, including where there is no feature, so the output is a noise field wherever pc is small. On load_synth_yeast_plate 89.6% of pixels have pc < 0.02; over those, "orientation" spans the full [0, 1] with std = 0.307 and only 3.3% lie near the 0.5 that means “vertical edge”. Kovesi consumes his or masked by pc (his comment: “Quantize to 0 - 180 degrees (for NONMAXSUP)”). Feed "pc" to a detector; read the angles for inspection, or mask them yourself.

"orientation"’s true image is (0, 1], not [0, 1]: the fold is half-open, so -pi/2 is unattainable. "feature_type" attains both ends.
norm (Literal['clip', 'rescale'] | None) – Output-range policy for output="pc". "clip" (default) saturates to [0, 1], "rescale" remaps the observed PC range to [0, 1], and None preserves it. It does not affect the two angle maps, whose fixed [0, 1] encoding would otherwise lose its physical meaning.

Returns:

Input image with detect_mat replaced by the selected monogenic map. PC output follows norm; angle outputs retain their normalized [0, 1] encoding. rgb and gray are unchanged.

Return type:

Image

Raises:

ValidationError – If n_scale < 2, min_wavelength < 2, mult <= 1, sigma_onf outside [0.1, 1.0], k < 0, deviation_gain <= 0, cutoff outside (0, 1), g <= 0, or output is not one of "pc", "orientation", "feature_type".

Examples

Enhance colony boundaries on a synthetic yeast plate. Phase congruency responds at colony rims regardless of how opaque each colony is:

>>> from phenotypic.data import load_synth_yeast_plate
>>> from phenotypic.enhance import FocusEdgeMonogenicPhase
>>> image = load_synth_yeast_plate()
>>> enhanced = FocusEdgeMonogenicPhase().apply(image)
>>> bool(enhanced.detect_mat[:].max() > 0.5)
True

Ask instead whether each feature is a step (a colony rim) or a line (a hypha or a scratch). 0.5 is a step edge:

>>> feature_type = FocusEdgeMonogenicPhase(output="feature_type")
>>> classified = feature_type.apply(load_synth_yeast_plate())
>>> bool(0.0 <= classified.detect_mat[:].min() <= classified.detect_mat[:].max() <= 1.0)
True

Note

This is a port of Kovesi’s phasecongmono. The field notebook attributes monogenic phase congruency to Wang Lijuan et al., CCDC 2014; that paper was not consulted and this operation does not claim to reproduce its formulation.

See also

FocusEdgePhase for the oriented log-Gabor bank, which additionally yields corner strength via the moment tensor.

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.
`n_scale`
`min_wavelength`
`mult`
`sigma_onf`
`k`
`deviation_gain`
`cutoff`
`g`
`noise_method`
`output`
`norm`

n_scale: Annotated[int, TuneSpec(3, 6)]#

min_wavelength: Annotated[float, TuneSpec(2.0, 10.0)]#

mult: Annotated[float, TuneSpec(1.5, 3.0)]#

sigma_onf: Annotated[float, TuneSpec(0.1, 0.99)]#

k: Annotated[float, TuneSpec(0.5, 20.0)]#

deviation_gain: Annotated[float, TuneSpec(1.0, 2.0)]#

cutoff: Annotated[float, TuneSpec(0.3, 0.7)]#

g: Annotated[float, TuneSpec(2.0, 20.0)]#

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

output: MonogenicOutput#

__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#

Move norm to the end of the subclass’s field order.

Parameters:: kwargs (Any)
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, inplace=False)#

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

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

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

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

!!! 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]

!!! 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 = {'cutoff': FieldInfo(annotation=float, required=False, default=0.5, description='Fractional frequency-spread below which the response is penalized, so that a feature excited at a single scale scores lower than a broadband one.', metadata=[Gt(gt=0.0), Lt(lt=1.0), TuneSpec(low=0.3, high=0.7, step=None, log=False, categories=None, tunable=True)]), 'deviation_gain': FieldInfo(annotation=float, required=False, default=1.5, description='Scales the phase-deviation term, sharpening edge localization.', metadata=[Gt(gt=0.0), TuneSpec(low=1.0, high=2.0, step=None, log=False, categories=None, tunable=True)]), 'g': FieldInfo(annotation=float, required=False, default=10.0, description='Sharpness of the frequency-spread sigmoid.', metadata=[Gt(gt=0.0), TuneSpec(low=2.0, high=20.0, step=None, log=False, categories=None, tunable=True)]), 'k': FieldInfo(annotation=float, required=False, default=3.0, description="Number of noise standard deviations above the mean at which the noise threshold sits. **``phasecongmono``'s default is 3.0**, not :class:`FocusEdgePhase`'s 2.0. Raise it on noisy scans.", metadata=[Ge(ge=0.0), TuneSpec(low=0.5, high=20.0, step=None, log=False, categories=None, tunable=True)]), 'min_wavelength': FieldInfo(annotation=float, required=False, default=3.0, description='Wavelength of the finest scale, in pixels. Raise it to ignore fine texture such as agar speckle.', metadata=[Ge(ge=2.0), TuneSpec(low=2.0, high=10.0, step=None, log=False, categories=None, tunable=True)]), 'mult': FieldInfo(annotation=float, required=False, default=2.1, description='Wavelength multiplier between successive scales. ``2.1`` with ``sigma_onf=0.55`` gives roughly two-octave filter bandwidths.', metadata=[Gt(gt=1.0), TuneSpec(low=1.5, high=3.0, step=None, log=False, categories=None, tunable=True)]), 'n_scale': FieldInfo(annotation=int, required=False, default=4, description='Number of log-Gabor scales. Must be at least 2 -- the frequency-spread weight divides by ``n_scale - 1``. More scales widen the frequency coverage at linear cost.', metadata=[Ge(ge=2), TuneSpec(low=3, high=6, step=None, log=False, categories=None, tunable=True)]), 'noise_method': FieldInfo(annotation=float, required=False, default=-1.0, description="``-1`` estimates the Rayleigh noise parameter from the median of the finest scale's amplitude; ``-2`` uses its histogram mode. Any value ``>= 0`` is used verbatim as the threshold, so ``0.0`` disables it.", metadata=[TuneSpec(low=None, high=None, step=None, log=False, categories=None, tunable=False)]), 'norm': FieldInfo(annotation=Union[Literal['clip', 'rescale'], NoneType], required=False, default='clip', description='Output-range policy for ``output="pc"``. ``"clip"`` (default) saturates to [0, 1], ``"rescale"`` remaps the observed PC range to [0, 1], and ``None`` preserves it. It does not affect the two angle maps, whose fixed [0, 1] encoding would otherwise lose its physical meaning.'), 'output': FieldInfo(annotation=Literal['pc', 'orientation', 'feature_type'], required=False, default='pc', description='Which map to write to ``detect_mat``. ``"pc"`` is the congruency in ``[0, 1]``. ``"orientation"`` and ``"feature_type"`` are angles in ``[-pi/2, pi/2]``, mapped to ``[0, 1]`` by ``(theta + pi/2)/pi``, since ``detect_mat`` must lie in the unit interval; invert the map to recover radians. For ``"orientation"``, ``0.5`` is a vertical edge and ``1.0`` a horizontal one. For ``"feature_type"``, ``0.5`` is a step edge, ``1.0`` a bright line and ``0.0`` a dark line. **The two angle maps are diagnostic, not detectable.** An angle is defined everywhere, including where there is no feature, so the output is a noise field wherever ``pc`` is small. On ``load_synth_yeast_plate`` 89.6% of pixels have ``pc < 0.02``; over those, ``"orientation"`` spans the full ``[0, 1]`` with ``std = 0.307`` and only 3.3% lie near the ``0.5`` that means "vertical edge". Kovesi consumes his ``or`` masked by ``pc`` (his comment: *"Quantize to 0 - 180 degrees (for NONMAXSUP)"*). Feed ``"pc"`` to a detector; read the angles for inspection, or mask them yourself. ``"orientation"``\'s true image is ``(0, 1]``, not ``[0, 1]``: the fold is half-open, so ``-pi/2`` is unattainable. ``"feature_type"`` attains both ends.'), 'sigma_onf': FieldInfo(annotation=float, required=False, default=0.55, description="Ratio of each filter's Gaussian sigma to its centre frequency. Smaller means narrower bandwidth, more scales needed for coverage.", metadata=[Ge(ge=0.1), Lt(lt=1.0), TuneSpec(low=0.1, high=0.99, 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

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

!!! 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

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.

norm: NormOut#

phenotypic.enhance.FocusEdgeMonogenicPhase#

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