phenotypic.abc_.GridMeasureFeatures#

class phenotypic.abc_.GridMeasureFeatures[source]#

Bases: MeasureFeatures, ABC

Extract feature measurements from detected colonies in GridImage objects.

GridMeasureFeatures is a type-safe wrapper around MeasureFeatures that enforces GridImage input type. It is to MeasureFeatures what GridOperation is to ImageOperation: a specialization for grid-aware (arrayed plate) analysis.

What is GridMeasureFeatures?

GridMeasureFeatures enables measurements that leverage well structure in arrayed plate images:

Type-safe wrapper: Enforces GridImage input (guaranteed valid grid structure with rows, cols, well positions).
Grid-aware measurements: Access per-well positions, row/column layout via image.grid.info(), enabling well-normalized and position-aware metrics.
Per-well metrics: Compute measurements per colony but with awareness of which well it belongs to, well size, and row/column position.
Relationship to MeasureFeatures: Inherits all methods from MeasureFeatures (parent class). Only difference is stricter input type validation (GridImage required) plus guaranteed access to grid information.
Output format: Returns pandas.DataFrame with one row per detected colony, matching image.objmap labels. Can easily map back to grid layout.
Well-normalized analysis: Enables computing metrics relative to well size/position (e.g., colony area as fraction of well area, normalized by well position on plate).

Purpose

Use GridMeasureFeatures when implementing measurement operations that extract quantitative metrics from colonies in grid-structured agar plate images. Like MeasureFeatures, it returns pandas DataFrames with one row per detected colony. The only difference is that it requires GridImage input, making explicit that your measurement may leverage grid structure (well positions, row/column layout) if desired.

GridImage vs Image

Image: Generic image with optional, unvalidated grid information.
GridImage: Specialized Image subclass with validated grid structure (row/column layout, well positions, grid alignment). Suitable for 96-well, 384-well, or other arrayed plate formats.

Quick Decision Guide

Choose GridMeasureFeatures vs MeasureFeatures:

GridMeasureFeatures: Measurement depends on or benefits from well structure. Examples: per-well metrics, well-normalized values, measurements filtered by well position, edge well exclusion.
MeasureFeatures: Measurement works equally well on any Image. Examples: colony size, color, morphology computed globally without well awareness or position dependency.
Type safety: GridMeasureFeatures enforces GridImage; MeasureFeatures accepts any Image (grid optional).
Well-level data: Use GridMeasureFeatures when you need image.grid.info() for per-well analysis and when grid structure is essential to your measurement.
Multi-well experiments: GridMeasureFeatures simplifies tracking which well each colony belongs to, row/column position, and inter-well comparisons.
Subclass reference: Both GridMeasureSize and GridMeasureShape inherit from GridMeasureFeatures.
Performance: GridMeasureFeatures has minimal overhead vs MeasureFeatures; use it when grid structure matters.
Flexibility: If uncertain whether you need grid structure, start with MeasureFeatures; upgrade to GridMeasureFeatures if well-aware logic becomes necessary.

Implementation Pattern

Inherit from GridMeasureFeatures and implement _operate() as an instance method:

from phenotypic.abc_ import GridMeasureFeatures
from phenotypic import GridImage
from phenotypic.schema import OBJECT
import pandas as pd

class GridMeasureWellOccupancy(GridMeasureFeatures):
    '''Measure fraction of well area occupied by colonies.'''

    normalize: bool = True  # Annotated class-level field

    def _operate(self, image: GridImage) -> pd.DataFrame:
        # image is guaranteed to be GridImage with validated grid structure
        grid_info = image.grid.info()  # Access well positions and layout
        # grid_info contains: 'grid_shape', 'well_centers', 'well_size', etc.

        nrows, ncols = grid_info['grid_shape']
        well_size = grid_info['well_size']

        # Calculate area occupied by colonies using MeasureFeatures methods
        area = self._calculate_sum(image.objmask[:], image.objmap[:])

        # Optional: normalize by well size for grid-aware analysis
        if self.normalize and well_size > 0:
            area = area / (well_size ** 2)

        # Build results DataFrame with colony labels (required by contract)
        results = pd.DataFrame({'WellArea': area})
        results.insert(0, OBJECT.LABEL, image.objects.labels2series())
        return results

Typical Use Cases

Per-well phenotypic analysis: Extract growth metrics where well position and size matter for normalization (e.g., center wells may grow differently from edge wells due to evaporation).
Grid-based filtering: Measure only colonies in specific well regions (e.g., measure only center wells in high-variance experiments, exclude border wells due to contamination risk).
Well-normalized metrics: Compute colony area relative to well size, colony density within well boundaries, or occupancy rates per well in a 96-well or 384-well plate.
Multi-well experiments: Track which well each colony occupies via grid.info(), enabling growth curve fitting per well and inter-well statistical comparisons.
Quality control filtering: Exclude edge wells from analysis where plate handling artifacts are common, or normalize measurements by well position.

Helper Methods Available

Inherit all measurement methods from MeasureFeatures parent class:

_calculate_sum() - Total measurement per object (e.g., total area)
_calculate_mean() - Average measurement per object (e.g., mean intensity)
_calculate_median() - Median measurement per object
_calculate_std() - Standard deviation per object
_calculate_percentile() - Percentile-based measurements (e.g., 95th percentile size)
_extract_properties_per_object() - Extract properties from regionprops or similar

Grid Information Available

Access grid structure via image.grid.info() in _operate():

'grid_shape' - Tuple (nrows, ncols) of well layout
'well_centers' - List of (row_px, col_px) well center coordinates in pixel space
'well_size' - Typical well size in pixels (varies by format: 96-well vs 384-well)
'rotation_angle' - Current grid rotation angle in degrees
'alignment' - Alignment state/quality metrics

Notes

The measure() method is inherited from MeasureFeatures; the only difference is input type validation (GridImage required).
Returns pandas.DataFrame with one row per detected object, first column is OBJECT.LABEL (matching image.objmap labels).
GridImage must have valid grid structure set before measuring. Typically set by GridFinder or GridCorrector operations in the pipeline.
All helper methods from MeasureFeatures (mean, median, sum, etc.) are available.
DataFrame returned can be easily mapped back to grid layout using image.grid.info().

Relationship to MeasureFeatures and GridOperation

MeasureFeatures: Parent class providing all measurement logic. GridMeasureFeatures enforces GridImage input and guarantees grid structure is available via image.grid.info().
GridOperation: Base class for all grid-aware operations. GridMeasureFeatures is a specialized subclass combining measurement functionality with grid awareness.
Pipeline integration: Typical order: detect_colonies (ObjectDetector) → measure_features (GridMeasureFeatures) → analyze_growth (e.g., growth curve fitting per well).

Well-to-Colony Mapping Patterns

Common workflows for mapping measurements back to grid:

Per-well aggregation: Group colonies by well position; compute statistics per well (mean, std, count).
Well-position filtering: Exclude edge wells, measure only center wells, or apply position-dependent normalization.
Row/column analysis: Group by row or column; detect spatial patterns or plate gradients.
Multi-well comparisons: Compare growth metrics across wells for identification of outliers or resistance.

Output DataFrame Structure

Standard format returned by GridMeasureFeatures._operate():

First column: OBJECT.LABEL (int) - Matches image.objmap labels, one row per colony
Measurement columns: Your custom measurements (Area, Intensity, Circularity, etc.)
Mapping to grid: Use image.grid.info()[‘well_centers’] + label → pixel position → well assignment

Validation Checklist

Before shipping GridMeasureFeatures subclass:

Verify grid.info() call succeeds and returns expected keys
Test on GridImage with known grid (use load_synth_yeast_plate())
Ensure DataFrame output matches expected column structure (OBJECT.LABEL first, then measurements)
Check that measurements make sense (e.g., area > 0, intensity in expected range)
Validate on both small (96-well) and large (384-well) plates if possible

Known Implementations

Reference implementations in the PhenoTypic framework:

GridMeasureSize: Measures colony size and morphological features per detected object in GridImage. Extends GridMeasureFeatures with size-specific methods and grid-aware normalization.
GridMeasureShape: Measures colony shape/circularity/symmetry features. Demonstrates how grid structure can influence shape interpretation (e.g., elongation along grid rows).
MeasureSize (MeasureFeatures): Parent class implementation. GridMeasureSize extends this with grid awareness.

Testing GridMeasureFeatures Implementations

Best practices for testing new GridMeasureFeatures subclasses:

Use load_synth_yeast_plate() from phenotypic.data (creates GridImage with detected colonies).
Verify DataFrame output structure: first column OBJECT.LABEL, subsequent columns are measurements.
Test with edge cases: image with no colonies detected, single large colony, multiple overlapping colonies.
Check well-mapping: ensure measurements map correctly to grid positions via image.grid.info().
Validate row/column filtering logic if using position-aware filtering (center wells, edge exclusion).

Examples

Grid-aware measurement of colony size per well:

>>> from phenotypic import GridImage
>>> from phenotypic.abc_ import GridMeasureFeatures
>>> from phenotypic.schema import OBJECT
>>> import pandas as pd
>>> class MeasureWellOccupancy(GridMeasureFeatures):
...     '''Measure total area occupied in each well.'''
...
...     def _operate(self, image: GridImage) -> pd.DataFrame:
...         # Use grid accessor to calculate per-well metrics
...         area = self._calculate_sum(image.objmask[:], image.objmap[:])
...         well_info = image.grid.info()  # Get well assignments
...         # Combine area with well location
...         results = pd.DataFrame({
...             'WellArea': area,
...         })
...         results.insert(0, OBJECT.LABEL, image.objects.labels2series())
...         return results
>>> # Usage
>>> from phenotypic import Image
>>> from phenotypic.detect import OtsuDetector
>>> image = Image('plate.jpg')
>>> image = OtsuDetector().operate(image)
>>> grid_image = GridImage(image)
>>> grid_image.detect_grid()  # Establish grid structure
>>> measurer = MeasureWellOccupancy()
>>> df = measurer.measure(grid_image)  # Returns grid-aware measurements

Grid-aware filtering: measure only center wells:

>>> from phenotypic.abc_ import GridMeasureFeatures
>>> from phenotypic import GridImage
>>> import pandas as pd
>>> class MeasureCenterWells(GridMeasureFeatures):
...     '''Measure size only for colonies in center wells (exclude edge wells).'''
...
...     def _operate(self, image: GridImage) -> pd.DataFrame:
...         grid_info = image.grid.info()
...         nrows, ncols = grid_info['grid_shape']
...         # Get all measurements first
...         all_areas = self._calculate_sum(image.objmask[:], image.objmap[:])
...         labels = image.objects.labels2series()
...         # Filter: keep only colonies in center wells
...         center_mask = self._get_center_well_mask(grid_info, nrows, ncols)
...         # Return measurements for center-well colonies only
...         results = pd.DataFrame({'Area': all_areas[center_mask]})
...         results.insert(0, 'OBJECT.LABEL', labels[center_mask])
...         return results

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`construct`
`copy`	Returns a copy of the model.
`dict`
`from_json`	Reconstruct an operation from JSON written by `to_json()`.
`from_orm`
`json`
`measure`	Compute grid edges and assign each detected object to a grid cell.
`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.

measure(image)[source]#

Compute grid edges and assign each detected object to a grid cell.

Parameters:: image – Image with detected objects.
Returns:: DataFrame with grid assignments (ROW_NUM, COL_NUM, ROW_MAJOR_IDX).

__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

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 = {}#

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.abc_.GridMeasureFeatures#

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