phenotypic.analysis.EdgeCorrector#

class phenotypic.analysis.EdgeCorrector(*, on: Annotated[str, _ColumnRefMarker('measurements')], groupby: Annotated[list[str], _ColumnRefMarker('measurements')], agg_func: Callable | str | list | dict | None = 'mean', n_jobs: int = 1, time_label: Annotated[str, _ColumnRefMarker('measurements')] = 'MetadataCulture_Time', nrows: int = 8, ncols: int = 12, connectivity: int = 4, top_n: int = 3, pvalue: float = 0.05)[source]#

Bases: EdgeCorrection

Analyzer for detecting and correcting edge effects in arrayed colony growth.

This class identifies colonies at grid edges (missing orthogonal neighbors) and caps their measurement values to prevent edge effects in high-throughput phenotyping assays. Edge colonies often show artificially inflated measurements (larger areas, higher color intensity) due to lack of competition for resources from missing neighbors. The corrector uses permutation testing to determine if edge and interior colonies are statistically different before applying correction.

Intuition: In plate-based assays (96-well, 384-well), colonies at grid edges experience fundamentally different growth conditions: they lack orthogonal neighbors that would otherwise compete for nutrients and space. This causes edge colonies to appear larger/brighter than interior colonies under identical conditions, biasing downstream analyses. EdgeCorrector detects this asymmetry and caps measurements to a threshold derived from top interior colonies, preventing this systematic bias.

Use cases:

High-throughput phenotyping on standard plate layouts (8x12, 16x24, etc.)

Growth assays where colony size/intensity is a fitness proxy

Comparing genotypes across plates with multiple replicates per condition

Any analysis where spatial position should not correlate with phenotype

Caveats:

Requires multiple interior colonies to establish a reliable threshold

Edge correction assumes interior and edge colonies should have similar distributions; this may not hold in some experimental designs

If too many wells are empty or dead, surrounded position detection may fail

Permutation testing requires adequate sample sizes for statistical power

All measurements (not just edge colonies) are capped when correction is applied

Attributes:
nrows (int): Number of rows in the grid layout. ncols (int): Number of columns in the grid layout. top_n (int): Number of top-valued interior colonies to use for threshold calculation. connectivity (int): Neighbor pattern: 4 (orthogonal) or 8 (with diagonals). time_label (str): Column name containing time point information. pvalue (float): P-value threshold for permutation test (0.0 disables test). on (str): Name of measurement column to analyze and correct. groupby (list[str]): Column names for grouping data by experiment/plate/condition.

Category: **EdgeCorrection**#
Name	Description	Type
`Cap`	The carrying capacity for the target measurement	Derived
`NewVal`	The new value of the target measurement	Derived

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`analyze`	Apply the edge-correction strategy group-by-group.
`construct`
`copy`	Returns a copy of the model.
`dash`	Interactive Plotly visualization of analysis results.
`dict`
`from_orm`
`json`
`model_construct`	Creates a new instance of the Model class with validated data.
`model_copy`	!!! abstract "Usage Documentation"
`model_dump`	!!! abstract "Usage Documentation"
`model_dump_json`	!!! abstract "Usage Documentation"
`model_json_schema`	Generates a JSON schema for a model class.
`model_parametrized_name`	Compute the class name for parametrizations of generic classes.
`model_post_init`	This function is meant to behave like a BaseModel method to initialise private attributes.
`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`
`results`	Return the corrected measurement DataFrame from the last analyze() call.
`schema`
`schema_json`
`show`	Visualize edge correction results with interior/edge colony comparisons.
`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.
`top_n`
`pvalue`
`time_label`
`nrows`
`ncols`
`connectivity`
`on`
`groupby`
`agg_func`
`n_jobs`

Parameters:

on (Annotated[str, _ColumnRefMarker('measurements')])
groupby (Annotated[list[str], _ColumnRefMarker('measurements')])
agg_func (Callable | str | list | dict | None)
n_jobs (int)
time_label (Annotated[str, _ColumnRefMarker('measurements')])
nrows (int)
ncols (int)
connectivity (int)
top_n (int)
pvalue (float)

top_n: int#

pvalue: float#

show(figsize: tuple[int, int] | None = None, max_groups: int = 20, collapsed: bool = True, criteria: dict[str, Any] | None = None, **kwargs) → tuple[Figure, TypeAliasForwardRef('matplotlib.axes.Axes')][source]#

Visualize edge correction results with interior/edge colony comparisons.

Displays the distribution of measurements for the last time point per group, highlighting interior (surrounded) vs. edge colonies. Shows the calculated correction threshold and permutation test p-values. Interior colonies are shown in blue, edge colonies in red. Circles indicate measurements passing the threshold, X’s indicate capped measurements.

Parameters:

figsize (tuple[int, int], optional) – Figure size as (width, height) in inches. If None, auto-sized based on number of groups (single-group: 10x6, many groups: 10x max(6, 0.5*ngroups+2)).
max_groups (int, optional) – Maximum number of groups to display. Defaults to 20. If data has more groups, a warning is printed and only the first 20 are shown.
collapsed (bool, optional) – If True (default), show all groups stacked vertically on a single axis with y-offsets. If False, create a grid of subplots with one group per subplot.
criteria (dict[str, Any], optional) – Filter groups before visualization using column-value criteria (e.g., {‘Plate’: ‘P1’, ‘Condition’: [‘WT’, ‘KO’]}). Filtering uses SetAnalyzer._filter_by with AND logic across criteria.
**kwargs –
Additional matplotlib parameters:
- dpi (int): Figure resolution, passed to plt.subplots()
- facecolor (str): Figure background color
- edgecolor (str): Figure edge color
- legend_fontsize (int): Font size for legend (default 9 for collapsed, 8 for individual)

Returns:

Tuple of (matplotlib Figure, Axes object(s)):

If collapsed=True: (Figure, single Axes)

If collapsed=False: (Figure, array of Axes)

Return type:

tuple[Figure, plt.Axes]

Raises:

RuntimeError – If analyze() has not been called (no results to display).
ValueError – If criteria filter leaves no matching data.

Notes

Interior colonies are those with all orthogonal neighbors present (4-connectivity)
Edge colonies are detected but lack all orthogonal neighbors
Threshold line (orange) is derived from top interior colonies
P-values displayed between interior and edge means (if pvalue != 0)
Permutation test uses 1000 resamples with two-sided alternative
Call analyze() before show()

Examples

Basic visualization of edge correction results:

>>> corrector = EdgeCorrector(on='Area', groupby=['ImageName'])
>>> corrected = corrector.analyze(data)
>>> fig, ax = corrector.show()
>>> # Single collapsed plot with all groups stacked vertically

Individual subplots per group:

>>> fig, axes = corrector.show(
...     collapsed=False,
...     figsize=(15, 10)
... )
>>> # Grid of subplots, max 3 columns

Filtered visualization for specific plate:

>>> fig, ax = corrector.show(
...     criteria={'Plate': 'P1'},
...     max_groups=10,
...     figsize=(12, 8)
... )

results() → pandas.DataFrame[source]#

Return the corrected measurement DataFrame from the last analyze() call.

Retrieves the DataFrame with edge-corrected measurements produced by the most recent call to analyze(). Provides convenient access to results without retaining a local reference.

Returns:

Edge-corrected measurements with original data plus two new: correction columns: - EDGE_CORRECTION.NEW_VAL-{self.on}: Capped measurement values - EDGE_CORRECTION.CORRECTED_CAP-{self.on}: Threshold value used Original measurement column (self.on) is preserved unchanged. If analyze() has not been called, returns an empty DataFrame.

Return type:

pd.DataFrame

Examples

Retrieving corrected measurements after analysis:

>>> corrector = EdgeCorrector(
...     on='Area',
...     groupby=['ImageName']
... )
>>> corrected = corrector.analyze(data)
>>> results = corrector.results()
>>> assert results.equals(corrected)
>>> # Access corrected values
>>> corrected_areas = results['Size-Area']
>>> thresholds = results['Cap-Area']
>>> # Original 'Area' column also available for comparison
>>> original_areas = results['Area']

Notes

Returns the DataFrame stored in self._latest_measurements
Same as the return value of analyze()
Always use this method rather than direct attribute access

__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

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, copying parameter descriptions parsed from the Google-style Args: docstring block onto each field’s description slot.

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

analyze(data: pandas.DataFrame) → pandas.DataFrame#

Apply the edge-correction strategy group-by-group.

Validates the frame, stores the pre-correction copy on self._original_data, aggregates to one row per well per group, then dispatches each group to the static _apply2group_func() worker (serial for a single group, joblib-parallel otherwise) using the kwargs from _group_config().

Parameters:: data (pandas.DataFrame)
Return type:: pandas.DataFrame

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(**kwargs)#

Interactive Plotly visualization of analysis results.

Subclasses may override this method to provide an interactive Plotly figure equivalent to show().

Raises:: NotImplementedError – Unless overridden by a subclass.

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_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 = {'agg_func': FieldInfo(annotation=Union[Callable, str, list, dict, NoneType], required=False, default='mean'), 'connectivity': FieldInfo(annotation=int, required=False, default=4, description='Neighbor pattern: 4 (orthogonal) or 8 (with diagonals).'), 'groupby': FieldInfo(annotation=list[str], required=True, description='Column names for grouping data by experiment/plate/condition.', metadata=[_ColumnRefMarker('measurements')]), 'n_jobs': FieldInfo(annotation=int, required=False, default=1, alias_priority=2, validation_alias=AliasChoices(choices=['n_jobs', 'num_workers'])), 'ncols': FieldInfo(annotation=int, required=False, default=12, description='Grid columns.'), 'nrows': FieldInfo(annotation=int, required=False, default=8, description='Grid rows.'), 'on': FieldInfo(annotation=str, required=True, description='Name of measurement column to analyze and correct.', metadata=[_ColumnRefMarker('measurements')]), 'pvalue': FieldInfo(annotation=float, required=False, default=0.05, description='P-value threshold for permutation test (0.0 disables test).'), 'time_label': FieldInfo(annotation=str, required=False, default='MetadataCulture_Time', description='Column holding the time point.', metadata=[_ColumnRefMarker('measurements')]), 'top_n': FieldInfo(annotation=int, required=False, default=3, description='Number of top-valued interior colonies to use for threshold calculation.')}#

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(context: Any, /) → None#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

self (BaseModel) – The BaseModel instance.
context (Any) – The context.

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

classmethod update_forward_refs(**localns: Any) → None#

Parameters:: localns (Any)
Return type:: None

classmethod validate(value: Any) → Self#

Parameters:: value (Any)
Return type:: Self

time_label: ColumnRef#

nrows: int#

ncols: int#

connectivity: int#

on: ColumnRef#

groupby: ColumnRefList#

agg_func: Callable | str | list | dict | None#

n_jobs: int#

phenotypic.analysis.EdgeCorrector#

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