phenotypic.abc_.GridFinder#

class phenotypic.abc_.GridFinder(*, nrows: int, ncols: int)[source]#

Bases: GridMeasureFeatures, ABC

Abstract base class for detecting grid structure and assigning objects to wells.

GridFinder is the foundation for grid detection algorithms in arrayed plate imaging. It detects the row and column spacing of colonies on agar plates and assigns each detected object to its corresponding grid cell (well). This is essential for high-throughput phenotyping experiments where samples are arranged in regular grids (e.g., 96-well, 384-well formats).

Quick Decision Guide

Use [AutoGridFinder](src/phenotypic/grid/_auto_grid_finder.py) when: - Grid position is unknown or image is rotated/shifted - Colonies are detected but well boundaries are unclear - You want automatic optimization of row/column edge positions - Tolerance parameter allows tuning optimization precision

Use [ManualGridFinder](src/phenotypic/grid/_manual_grid_finder.py) when: - You know exact grid geometry from microscope calibration - Grid position is fixed and repeatable across images - You have pre-measured row and column edge coordinates - You want deterministic, non-optimized grid assignment

Combining with detection pipelines: - Use GridFinder after ObjectDetector to map colonies to wells - AutoGridFinder works with any detection result - ManualGridFinder requires pre-computed edge coordinates - Grid assignment is independent of detection algorithm

What it does

GridFinder implementations analyze the spatial distribution of detected objects in an image and determine the underlying grid structure. They compute pixel coordinates where grid rows and columns are located (row_edges and col_edges), then use these edges to assign each object to a row number, column number, and section number (unique well identifier).

Why it’s important for colony phenotyping

In arrayed plate experiments, colonies are grown at fixed positions corresponding to wells in a microplate. By mapping detected colonies to grid positions, downstream analysis can:

Sample tracking: Correlate colony measurements with sample metadata inoculated in each well
Replicate analysis: Track growth across identical replicate wells
Spatial detection: Identify contamination patterns or edge effects
Data export: Organize results by well coordinates for database import and statistical analysis

Without grid assignment, measurements are just unorganized lists of objects with no link to experimental design.

Grid concepts

Row edges: Array of pixel row coordinates marking row boundaries. For 8 rows, array has 9 values: [0, y1, y2, …, y8, image_height]. Objects between row_edges[i] and row_edges[i+1] belong to row i.
Column edges: Array of pixel column coordinates marking column boundaries. For 12 columns, array has 13 values: [0, x1, x2, …, x12, image_width]. Objects between col_edges[j] and col_edges[j+1] belong to column j.
Grid cell assignment: Each object’s centroid (center_rr, center_cc) is tested against row/column edges using pd.cut(), assigning it to row i (0 to nrows-1) and column j (0 to ncols-1).
Section number: A unique well ID computed as row*ncols + col, ordered left-to-right, top-to-bottom (top-left well = 0, bottom-right well = nrows*ncols - 1).

Typical plate formats

96-well plate: 8 rows × 12 columns (A1-H12)
384-well plate: 16 rows × 24 columns (A1-P24)
1536-well plate: 32 rows × 48 columns (very high-throughput)

Attributes:: nrows (int): Number of rows in the grid. For 96-well plates, typically 8. ncols (int): Number of columns in the grid. For 96-well plates, typically 12.

Abstract Methods

Subclasses must implement these methods:

_operate(image: Image) -> pd.DataFrame: Main entry point. Compute row and column edges, then call _get_grid_info() to assemble the complete grid DataFrame. Return the DataFrame with all grid assignments.
get_row_edges(image: Image) -> np.ndarray: Return array of row edge pixel coordinates. Length must be exactly nrows + 1 (e.g., 9 values for 8 rows).
get_col_edges(image: Image) -> np.ndarray: Return array of column edge pixel coordinates. Length must be exactly ncols + 1 (e.g., 13 values for 12 columns).

Helper Methods for Implementation

These protected methods reduce code duplication and handle grid assignment:

_get_grid_info(image, row_edges, col_edges) -> pd.DataFrame: Assembles complete grid information from pre-computed edge coordinates. Calls internal methods to populate ROW_NUM, COL_NUM, and ROW_MAJOR_IDX columns. Use this after computing edges in your _operate() implementation.
_add_row_number_info(): Assigns row indices using pd.cut() with object centroids and row edges.
_add_col_number_info(): Assigns column indices using pd.cut() with object centroids and column edges.
_add_section_number_info(): Computes section numbers from row and column indices using vectorized operations.
_clip_row_edges() / _clip_col_edges(): Ensures edge coordinates are clipped to image bounds (prevents indexing errors).

Output Format

The _operate() method returns a pandas DataFrame with detected objects and their grid assignments:

ROW_NUM: Grid row index (0 to nrows-1), representing vertical well position
COL_NUM: Grid column index (0 to ncols-1), representing horizontal well position
ROW_MAJOR_IDX: Well identifier (0 to nrows*ncols-1), ordered left-to-right, top-to-bottom for convenient database mapping
Additional columns: Object metadata (centroid, bounding box, morphology) from image.objects.info()

Objects whose centers fall outside the grid edges are clamped to the nearest edge cell (row 0 or nrows-1, col 0 or ncols-1).

Concrete Implementations

PhenoTypic provides two built-in GridFinder implementations:

[AutoGridFinder](src/phenotypic/grid/_auto_grid_finder.py): Deterministic robust fit using weighted object centers. Estimates pitch from center range, fits grid indices via least-squares, rejects outliers, and refits. Robust to protruding colonies (e.g., filamentous fungi). Use when grid position is unknown.
[ManualGridFinder](src/phenotypic/grid/_manual_grid_finder.py): User specifies exact row and column edge coordinates from calibration or measurement. Use when grid geometry is known and fixed.

Fitting Strategy (for AutoGridFinder)

AutoGridFinder uses a deterministic center-based robust fit:

Centers: Extract weighted centroids from detected objects, sorted along each axis
Pitch estimate: (max_center - min_center) / (n_expected - 1) (robust to multiple objects per cell)
Grid indices: round((center - min_center) / pitch)
Least-squares fit: center = pitch * idx + offset via closed-form normal equations
Outlier rejection: Remove centers where |residual| > pitch * residual_fraction
Refit: Refined pitch and offset from inliers only
Symmetry anchoring: When detected span < expected, center the grid in the image
Edges: offset + pitch * i - pitch/2 for i = 0..n, clipped to image bounds

Notes

GridFinder subclasses work with regular Image objects (not just GridImage)
Edge coordinates must be sorted in ascending order (handled by _clip_row_edges and _clip_col_edges)
Ensure row_edges and col_edges are clipped to image bounds to prevent indexing errors
Grid assignment uses pandas.cut() with include_lowest=True and right=True, meaning objects are assigned based on which interval they fall into
NaN values in grid columns indicate objects outside all grid cells

Examples

Use AutoGridFinder when grid position is unknown:

When the image is rotated, shifted, or geometry is unclear, let AutoGridFinder automatically compute optimal edge positions by optimizing alignment:

>>> from phenotypic.grid import AutoGridFinder
>>> from phenotypic.data import load_synth_yeast_plate
>>> from phenotypic.detect import OtsuDetector
>>> # Load and detect colonies on plate
>>> image = load_synth_yeast_plate()
>>> detector = OtsuDetector()
>>> image_with_objects = detector.apply(image)
>>> # AutoGridFinder optimizes edge positions to align with colonies
>>> grid_finder = AutoGridFinder(nrows=8, ncols=12)
>>> grid_df = grid_finder.measure(image_with_objects)
>>> # Access well assignments
>>> print(f"Found {len(grid_df)} colonies assigned to grid")
>>> print(grid_df[['ROW_NUM', 'COL_NUM', 'ROW_MAJOR_IDX']].head())

Create a ManualGridFinder for a 96-well plate with known geometry:

When grid geometry is known from microscope calibration, manually specify row and column edges for reproducible grid assignment:

>>> import numpy as np
>>> from phenotypic.grid import ManualGridFinder
>>> from phenotypic.data import load_synth_yeast_plate
>>> from phenotypic.detect import OtsuDetector
>>> # Load and detect colonies
>>> image = load_synth_yeast_plate()
>>> detector = OtsuDetector()
>>> image_with_objects = detector.apply(image)
>>> # Define grid for 8 rows x 12 columns (96-well)
>>> # Rows: 8 wells vertically, evenly spaced from pixel 100 to 2000
>>> row_edges = np.linspace(100, 2000, 9, dtype=int)
>>> # Columns: 12 wells horizontally, evenly spaced from pixel 50 to 3050
>>> col_edges = np.linspace(50, 3050, 13, dtype=int)
>>> # Create grid finder with known edge coordinates
>>> grid_finder = ManualGridFinder(row_edges=row_edges, col_edges=col_edges)
>>> grid_df = grid_finder.measure(image_with_objects)
>>> # Result includes grid assignments plus object metadata
>>> print(grid_df[['ROW_NUM', 'COL_NUM', 'ROW_MAJOR_IDX']].head())

Understanding ROW_MAJOR_IDX for well mapping:

ROW_MAJOR_IDX provides a single integer ID for each well, useful for organizing results and correlating with sample metadata:

>>> from phenotypic.grid import AutoGridFinder
>>> from phenotypic.data import load_synth_yeast_plate
>>> from phenotypic.detect import OtsuDetector
>>> # Detect and assign colonies to grid
>>> image = load_synth_yeast_plate()
>>> detector = OtsuDetector()
>>> image_with_objects = detector.apply(image)
>>> grid_finder = AutoGridFinder(nrows=8, ncols=12)
>>> grid_df = grid_finder.measure(image_with_objects)
>>> # Example: 8x12 grid (96-well plate)
>>> # ROW_MAJOR_IDX runs 0-95, numbered left-to-right, top-to-bottom
>>> # Section 0 = Row 0, Col 0 (top-left, A1)
>>> # Section 11 = Row 0, Col 11 (top-right, A12)
>>> # Section 12 = Row 1, Col 0 (second row left, B1)
>>> # Section 95 = Row 7, Col 11 (bottom-right, H12)
>>> # Filter colonies in a specific well
>>> section_5_objects = grid_df[grid_df['ROW_MAJOR_IDX'] == 5]
>>> # Map section numbers back to well coordinates
>>> well_row = 5 // 12  # Row index
>>> well_col = 5 % 12   # Column index

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`
`get_col_edges`	This method is to returns the column edges of the grid as a numpy array.
`get_row_edges`	This method is to returns the row edges of the grid as a numpy array.
`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.
`nrows`
`ncols`

Parameters:

nrows (int)
ncols (int)

nrows: int#

ncols: int#

abstractmethod get_row_edges(image: Image) → np.ndarray[source]#

This method is to returns the row edges of the grid as a numpy array. :param image: Image object. :type image: Image

Returns:: Row-edges of the grid.
Return type:: np.ndarray
Parameters:: image (Image)

abstractmethod get_col_edges(image: Image) → np.ndarray[source]#

This method is to returns the column edges of the grid as a numpy array. :param image:

Returns:: Column-edges of the grid.
Return type:: np.ndarray
Parameters:: image (Image)

__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

measure(image)#

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).

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 = {'ncols': FieldInfo(annotation=int, required=True), 'nrows': FieldInfo(annotation=int, required=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

phenotypic.abc_.GridFinder#

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