phenotypic.measure.MeasureSymmetricZones#

class phenotypic.measure.MeasureSymmetricZones(*, n_annuli: int = 100, pelt_penalty: float = 5.0, symmetry_threshold: float = 0.6666666666666666, n_angular_bins: int = 6, smoothing_window: int = 3, method: Literal['distance', 'intensity'] = 'distance', extent_margin: float = 0.05, min_samples_per_ring: int = 5, tau_core: float = 0.9, tau_dense: float = 0.5, tau_sparse: float = 0.1, intensity_source: Literal['gray', 'detect_mat'] = 'gray')[source]#

Bases: MeasureFeatures, FigureProvider

Measure colony radial expansion and angular symmetry from the object mask alone.

Quantifies each colony by four scalars derived directly from its binary mask and distance-from-inoculum map — no skeletonization, no branch tracing, no runner outlier flagging. The headline output is SymmetricRadius, the first radius past the inoculum core at which the per-annulus circular mean resultant length of mask-boundary pixels drops below a tunable symmetry threshold. CoreRadius (PELT changepoint on the radial density profile) anchors the measurement; MeanExpansion and MaxExpansion summarise how far growth reached past that core.

Zone segmentation (core / dense / sparse) uses a 1D per-annulus normalised colony-ness signal c(r) computed from the ring-wide mean intensity. After calibrating I_core and I_agar from percentiles of the expanded crop, each ring’s mean intensity is mapped into [0, 1] where 1 = pure colony and 0 = pure agar. For a roughly circular colony, c(r) decreases monotonically outward (uniform dense core ≈ 1, mixed dense branching ≈ 0.5–0.8, sparse branching ≈ 0.1–0.4, agar = 0), so zone boundaries follow directly from threshold crossings — no peak-finding. Zones are emitted as concentric circles at the three scalar radii. Variance and raw mean per ring are retained in the intermediates for diagnostic access but do not drive segmentation.

The mask’s only role in this signal is to (1) isolate which colony is being analysed, (2) seed the inoculum centre as the peak of the in-mask Euclidean distance transform, and (3) detect the colony-vs-agar intensity direction so the normalisation handles dark colonies (gray convention) and bright colonies (detect_mat convention) uniformly. The ring accumulation extends out to r_max = max_mask_radius × (1 + extent_margin) regardless of mask boundaries, so background pixels contribute truthfully to the signal.

Args:

n_annuli: Number of equal-area annuli used for the radial density
profile and angular analysis. Defaults to 100.

pelt_penalty: PELT penalty controlling changepoint sensitivity for
core detection. Defaults to 5.0.

symmetry_threshold: Minimum angular coverage (fraction of angular
bins occupied) for growth to be considered symmetric. Defaults to 4/6 (~0.667); with 6 angular bins this means at least 4 of 6 60-degree sectors must contain mask pixels.

n_angular_bins: Number of angular bins used to compute the
per-annulus angular coverage diagnostic. Defaults to 36 (10 degree resolution).

smoothing_window: Moving-average window (in annuli) applied to the
angular R̄ profile before the threshold test. Defaults to 3.

method: Inoculum centre estimator — "distance" uses the peak
of the Euclidean distance transform, "intensity" uses the intensity-weighted centroid. Defaults to "distance".

extent_margin: Fractional expansion of the ring accumulator past
the farthest mask pixel, so the outer annuli sample a small agar tail for the I_agar reference. Defaults to 0.05 (5%) — deliberately small because tight plates risk touching neighbouring colonies.

min_samples_per_ring: Minimum pixel count required to compute
a mean for a given ring; below this the ring’s mean is filled by linear interpolation from its neighbours before normalisation. Defaults to 5.

tau_core: Colony-ness threshold that marks the core/dense
boundary. The core zone extends out to the last ring where c(r) ≥ tau_core. Defaults to 0.9 (last “≥90% colony” ring).

tau_dense: Colony-ness threshold that marks the dense/sparse
boundary. The dense zone extends to the last ring where c(r) ≥ tau_dense. Defaults to 0.5 (last “majority colony” ring).

tau_sparse: Colony-ness threshold that marks the sparse/outside
boundary. The sparse zone extends to the last ring where c(r) ≥ tau_sparse, capped at the mask envelope. Defaults to 0.1.

intensity_source: Image array used for the mean-intensity
calculation – "gray" uses the grayscale (dark = colony), "detect_mat" uses the detection matrix (bright = colony). Direction is auto-detected. Defaults to "gray".

Returns:
pd.DataFrame: Object-level radial symmetry measurements with columns:

Object_Label: unique object identifier.

SymZones_CoreRadius: inoculum core radius (pixels).

SymZones_SymmetricRadius: first radius past the core where R̄ exceeds the symmetry threshold (pixels).

SymZones_MeanExpansion: mean boundary-pixel distance beyond the core (pixels, clamped at 0).

SymZones_MaxExpansion: maximum mask-pixel distance beyond the core (pixels, clamped at 0).

SymZones_CoreEndRadius: mean per-angle core boundary radius from the bright-fraction outward walk (pixels).

SymZones_DenseEndRadius: mean per-angle outer radius of the dense branching zone (pixels).

SymZones_SparseEndRadius: mean per-angle outer radius of the sparse branching zone, capped at the symmetric envelope (pixels).

SymZones_CoreArea: pixel^2 area of the inoculum core zone integrated across the 360-sector polar polygon.

SymZones_DenseArea: pixel^2 area of the dense branching zone (annular region between core and dense boundaries).

SymZones_SparseArea: pixel^2 area of the sparse branching zone (annular region between dense and outer boundaries).

Best For:

Summarising colony-level radial growth with a single symmetry figure of merit.

Distinguishing uniformly-expanding colonies from those with sectors, lopsided growth, or directional bias.

Comparing wild-type versus mutant expansion phenotypes when the biological question is about the colony envelope, not individual hyphae.

Time-course assays where runner counts are noisy but colony extent is informative.

Consider Also:

MeasureShape for general morphological descriptors (circularity, eccentricity) that do not require radial analysis.

MeasureBounds for lightweight bounding-box data without any radial pipeline.

See Also:
Tutorial 7: Measuring and Exporting for a walkthrough of measuring and exporting colony data.

Category: **SymZones**#
Name	Description	Type
`CoreRadius`	Radius of the dense inoculum core, determined by PELT changepoint detection on the radial mask-density profile centered on the inoculum. Growth measurements are reported relative to this boundary.	Tier 2 · Descriptive trait
`SymmetricRadius`	Radial distance from the inoculum centroid at which colony growth ceases to be angularly uniform. Computed as the first radius past the core where the smoothed per-annulus circular mean resultant length of mask-boundary pixels exceeds the symmetry threshold. Equals the colony outer envelope when growth remains symmetric throughout.	Tier 2 · Descriptive trait
`MeanExpansion`	Mean distance of mask-boundary pixels from the inoculum centroid, measured from the core boundary outward. Captures the typical radial extent of growth past the inoculum, averaged over all angular directions.	Tier 2 · Descriptive trait
`MaxExpansion`	Maximum distance of any mask pixel from the inoculum centroid, measured from the core boundary outward. Captures the farthest extent of growth past the inoculum.	Tier 2 · Descriptive trait
`CoreEndRadius`	Mean radius of the inoculum core boundary derived from the per-angle bright/background ratio walk. Each of 360 1° angular sectors finds the outer edge of the contiguous core run (bright fraction >= tau_core); the reported value is the mean across sectors. Compare with CoreRadius (the global PELT changepoint) — close agreement indicates a well-formed core.	Tier 2 · Descriptive trait
`DenseEndRadius`	Mean outer radius of the dense branching zone, where mask-bright pixels dominate (bright fraction >= tau_sparse). Per-angle radii are capped at the SymmetricRadius and angularly median-smoothed before averaging.	Tier 2 · Descriptive trait
`SparseEndRadius`	Mean outer radius of the sparse branching zone (= colony envelope inside the symmetric growth front). Equals min(objmask outer envelope, SymmetricRadius) per angle, averaged across 360 sectors.	Tier 2 · Descriptive trait
`CoreArea`	Pixel^2 area of the inoculum core zone, integrated across the 360-sector polar polygon defined by the per-angle core radii.	Tier 2 · Descriptive trait
`DenseArea`	Pixel^2 area of the dense branching zone, the annular region between the per-angle core boundary and dense-branching boundary.	Tier 2 · Descriptive trait
`SparseArea`	Pixel^2 area of the sparse branching zone, the annular region between the per-angle dense boundary and the symmetric-envelope outer boundary.	Tier 2 · Descriptive trait

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`construct`
`copy`	Returns a copy of the model.
`dash`	The interactive view.
`dict`
`figures`	Bind `subject` → a transient `BoundFigures` the Dash adapter consumes.
`from_json`	Reconstruct an operation from JSON written by `to_json()`.
`from_orm`
`inspect`	Plate-level diagnostic overlay for symmetric-radius measurement.
`iter_figures`	All `@figure` specs on this instance's class, in definition order.
`json`
`measure`	Execute the measurement operation on a detected-object image.
`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`

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_annuli`
`pelt_penalty`
`symmetry_threshold`
`n_angular_bins`
`smoothing_window`
`method`
`extent_margin`
`min_samples_per_ring`
`tau_core`
`tau_dense`
`tau_sparse`
`intensity_source`

Parameters:

n_annuli (int)
pelt_penalty (float)
symmetry_threshold (float)
n_angular_bins (int)
smoothing_window (int)
method (Literal['distance', 'intensity'])
extent_margin (float)
min_samples_per_ring (int)
tau_core (float)
tau_dense (float)
tau_sparse (float)
intensity_source (Literal['gray', 'detect_mat'])

n_annuli: int#

pelt_penalty: float#

symmetry_threshold: float#

n_angular_bins: int#

smoothing_window: int#

method: Literal['distance', 'intensity']#

extent_margin: float#

min_samples_per_ring: int#

tau_core: float#

tau_dense: float#

tau_sparse: float#

intensity_source: Literal['gray', 'detect_mat']#

inspect(image: Image | None = None, base_layer: Literal['rgb', 'gray', 'detect_mat'] = 'gray', *, for_save: bool = False)[source]#

Plate-level diagnostic overlay for symmetric-radius measurement.

Parameters:

image (Image | None) – Detected Image with objmap/objmask. If None, the image cached by the most recent measure() call is reused.
base_layer (Literal['rgb', 'gray', 'detect_mat']) – Which image array to use as the plotly background.
for_save (bool) – When True, every overlay trace is force-shown (no visible="legendonly") so the figure renders meaningfully as a static raster. The CLI’s --save-inspect flag passes this. Defaults to False (interactive Jupyter use, with overlay layers toggleable from the legend).

Returns:

plotly.graph_objects.Figure with toggleable overlay layers. Renders natively in Jupyter via the plotly mime bundle. For scroll-to-zoom, call fig.show(config={"scrollZoom": True}).

__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

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

dash(subject: Any = None) → Any#

The interactive view.

No Control anywhere → a composed subplot go.Figure (preserving the repo-wide .dash() -> go.Figure contract).
Any Control present → the ipywidgets notebook dashboard.

Parameters:: subject (Any) – Subject to bind (operations); None uses the held subject.
Returns:: A go.Figure (control-free) or an ipywidgets widget (controls).
Return type:: 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]

figures(subject: Any = None) → BoundFigures#

Bind subject → a transient BoundFigures the Dash adapter consumes. The per-render cache lives on the returned object, not here.

Parameters:: subject (Any)
Return type:: BoundFigures

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

iter_figures() → list[FigureSpec]#

All @figure specs on this instance’s class, in definition order.

Walks the MRO so inherited figures are included. Normal Python override semantics apply: a subclass method without @figure shadows and removes the inherited figure, while a redecorated override keeps the inherited figure’s original position.

Return type:: list[FigureSpec]

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

measure(image, include_meta=False)#

Execute the measurement operation on a detected-object image.

This is the main public API method for extracting measurements. It handles: input validation, parameter extraction via introspection, calling the subclass-specific _operate() method, optional metadata merging, and exception handling.

How it works (for users):

Pass your processed Image (with detected objects) to measure()
The method calls your subclass’s _operate() implementation
Results are validated and returned as a pandas DataFrame
If include_meta=True, image metadata (filename, grid info) is merged in

How it works (for developers):

When you subclass MeasureFeatures, you only implement _operate(). This measure() method automatically:

Calls _operate(), which reads its parameters from self
Validates the Image has detected objects (objmap)
Wraps exceptions in OperationFailedError with context
Merges grid/object metadata if requested

Parameters:

image (Image) – A PhenoTypic Image object with detected objects (must have non-empty objmap from a prior detection operation).
include_meta (bool, optional) – If True, merge image metadata columns (filename, grid position, etc.) into the results DataFrame. Defaults to False.

Returns:

Measurement results with structure:

First column: OBJECT.LABEL (integer IDs from image.objmap[:])

Remaining columns: Measurement values (float, int, or string)

One row per detected object

If include_meta=True, additional metadata columns are prepended before OBJECT.LABEL (e.g., Filename, GridRow, GridCol).

Return type:

pd.DataFrame

Raises:

OperationFailedError – If _operate() raises any exception, it is caught and re-raised as OperationFailedError with details including the original exception type, message, image name, and operation class. This provides consistent error handling across all measurers.

Notes

This method is the main entry point; do not override in subclasses
Subclasses implement _operate() only, not this method
Automatic memory profiling is available via logging configuration
Image must have detected objects (image.objmap should be non-empty)

Examples

Basic measurement extraction:

>>> from phenotypic import Image
>>> from phenotypic.measure import MeasureSize
>>> from phenotypic.detect import OtsuDetector
>>> # Load and detect
>>> image = Image('plate.jpg')
>>> image = OtsuDetector().operate(image)
>>> # Extract measurements
>>> measurer = MeasureSize()
>>> df = measurer.measure(image)
>>> print(df.head())

Include metadata in measurements:

>>> # With image metadata (filename, grid info)
>>> df_with_meta = measurer.measure(image, include_meta=True)
>>> print(df_with_meta.columns)
# Output: ['Filename', 'GridRow', 'GridCol', 'OBJECT.LABEL',
#          'Area', 'IntegratedIntensity', ...]

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 = {'extent_margin': FieldInfo(annotation=float, required=False, default=0.05, description='Fractional expansion of the ring accumulator past the farthest mask pixel, so the outer annuli sample a small agar tail for the ``I_agar`` reference. Defaults to 0.05 (5%) — deliberately small because tight plates risk touching neighbouring colonies.'), 'intensity_source': FieldInfo(annotation=Literal['gray', 'detect_mat'], required=False, default='gray', description='Image array used for the mean-intensity calculation -- ``"gray"`` uses the grayscale (dark = colony), ``"detect_mat"`` uses the detection matrix (bright = colony). Direction is auto-detected. Defaults to ``"gray"``.'), 'method': FieldInfo(annotation=Literal['distance', 'intensity'], required=False, default='distance', description='Inoculum centre estimator --- ``"distance"`` uses the peak of the Euclidean distance transform, ``"intensity"`` uses the intensity-weighted centroid. Defaults to ``"distance"``.'), 'min_samples_per_ring': FieldInfo(annotation=int, required=False, default=5, description="Minimum pixel count required to compute a mean for a given ring; below this the ring's mean is filled by linear interpolation from its neighbours before normalisation. Defaults to 5."), 'n_angular_bins': FieldInfo(annotation=int, required=False, default=6, description='Number of angular bins used to compute the per-annulus angular coverage diagnostic. Defaults to 36 (10 degree resolution).'), 'n_annuli': FieldInfo(annotation=int, required=False, default=100, description='Number of equal-area annuli used for the radial density profile and angular analysis. Defaults to 100.'), 'pelt_penalty': FieldInfo(annotation=float, required=False, default=5.0, description='PELT penalty controlling changepoint sensitivity for core detection. Defaults to 5.0.'), 'smoothing_window': FieldInfo(annotation=int, required=False, default=3, description='Moving-average window (in annuli) applied to the angular R̄ profile before the threshold test. Defaults to 3.'), 'symmetry_threshold': FieldInfo(annotation=float, required=False, default=0.6666666666666666, description='Minimum angular coverage (fraction of angular bins occupied) for growth to be considered symmetric. Defaults to 4/6 (~0.667); with 6 angular bins this means at least 4 of 6 60-degree sectors must contain mask pixels.'), 'tau_core': FieldInfo(annotation=float, required=False, default=0.9, description='Colony-ness threshold that marks the core/dense boundary. The core zone extends out to the last ring where ``c(r) ≥ tau_core``. Defaults to 0.9 (last "≥90% colony" ring).'), 'tau_dense': FieldInfo(annotation=float, required=False, default=0.5, description='Colony-ness threshold that marks the dense/sparse boundary. The dense zone extends to the last ring where ``c(r) ≥ tau_dense``. Defaults to 0.5 (last "majority colony" ring).'), 'tau_sparse': FieldInfo(annotation=float, required=False, default=0.1, description='Colony-ness threshold that marks the sparse/outside boundary. The sparse zone extends to the last ring where ``c(r) ≥ tau_sparse``, capped at the mask envelope. Defaults to 0.1.')}#

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

phenotypic.measure.MeasureSymmetricZones#

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