phenotypic.analysis.MADOutlierRemover#

class phenotypic.analysis.MADOutlierRemover(*, on: Annotated[str, _ColumnRefMarker('measurements')], groupby: Annotated[list[str], _ColumnRefMarker('measurements')], agg_func: Callable | str | list | dict | None = None, n_jobs: int = 1, threshold: float = 3.5)[source]#

Bases: SetAnalyzer

Analyzer for removing outliers using the modified Z-score (MAD) method.

This class removes outliers from measurement data by applying the Iglewicz-Hoaglin modified Z-score test within groups. For each group it computes the median and the median absolute deviation (MAD) of the measurement column, scores every row as 0.6745 * |value - median| / MAD, and removes rows whose score exceeds threshold.

Unlike Tukey’s fence (see TukeyOutlierRemover), the MAD method estimates spread from the median absolute deviation, which has a 50% breakdown point: the test stays accurate even when up to half the rows in a group are contaminated. This makes it a robust default for small or skewed colony-measurement groups.

If MAD is zero for a group (all values identical, or a > 50% tie), the test falls back to the raw absolute deviation from the median scaled by the mean absolute deviation, preserving the breakdown point while avoiding division by zero. If every value is identical, no rows are removed.

Parameters:

on (Annotated[str, _ColumnRefMarker('measurements')]) – Name of measurement column to test for outliers (e.g., ‘Shape_Area’, ‘Intensity_IntegratedIntensity’).
groupby (Annotated[list[str], _ColumnRefMarker('measurements')]) – List of column names to group by (e.g., [‘StrainID’, ‘Time’]).
threshold (float) – Modified Z-score cutoff. Iglewicz & Hoaglin (1993) recommend 3.5 for general use. Lower values (e.g., 2.5) are more aggressive; higher values (e.g., 5.0) more conservative. Default is 3.5.
n_jobs (int) – Number of parallel workers. Default is 1.
agg_func (Callable | str | list | dict | None)

on#

Column to test for outliers.

Type:: ColumnRef

groupby#

List of column names to group by.

Type:: ColumnRefList

threshold#

Modified Z-score cutoff used for outlier identification.

Type:: float

n_jobs#

Number of parallel workers. Default is 1.

Type:: int

Examples

Remove outliers and visualize results:

>>> import pandas as pd
>>> import numpy as np
>>> from phenotypic.analysis import MADOutlierRemover
>>> # Create sample data with some outliers
>>> np.random.seed(42)
>>> data = pd.DataFrame({
...     'ImageName': ['img1'] * 50 + ['img2'] * 50,
...     'Area': np.concatenate([
...         np.random.normal(200, 30, 48),
...         [500, 550],  # outliers in img1
...         np.random.normal(180, 25, 48),
...         [50, 600]  # outliers in img2
...     ])
... })
>>> # Initialize detector
>>> detector = MADOutlierRemover(
...     on='Area',
...     groupby=['ImageName'],
...     threshold=3.5
... )
>>> # Remove outliers
>>> filtered_data = detector.analyze(data)
>>> # Check how many were removed
>>> print(f"Original: {len(data)}, Filtered: {len(filtered_data)}")
>>> # Visualize removed outliers
>>> fig = detector.show()

References

Iglewicz, B., & Hoaglin, D. C. (1993). How to Detect and Handle Outliers. ASQC Quality Press.

Methods

`__init__`	Create a new model by parsing and validating input data from keyword arguments.
`analyze`	Remove outliers from data using the modified Z-score (MAD) method.
`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 filtered results (outliers removed).
`schema`
`schema_json`
`show`	Visualize outlier detection results.
`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.
`agg_func`
`threshold`
`on`
`groupby`
`n_jobs`

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

threshold: float#

analyze(data: pandas.DataFrame) → pandas.DataFrame[source]#

Remove outliers from data using the modified Z-score (MAD) method.

This method processes the input DataFrame by grouping according to specified columns and removing outliers within each group independently. Outliers are identified using the Iglewicz-Hoaglin modified Z-score and filtered out. The original data is stored internally for visualization purposes.

Parameters:

data (pandas.DataFrame) – DataFrame containing measurement data. Must include all columns specified in self.groupby and self.on.

Returns:

DataFrame with outliers removed. Contains only the original columns (no additional outlier flag columns).

Raises:

KeyError – If required columns are missing from input DataFrame.
ValueError – If data is empty or malformed.

Return type:

pandas.DataFrame

Examples

Analyze and filter outliers from measurement data:

>>> import pandas as pd
>>> import numpy as np
>>> from phenotypic.analysis import MADOutlierRemover
>>> # Create sample data
>>> np.random.seed(42)
>>> data = pd.DataFrame({
...     'ImageName': ['img1'] * 100,
...     'Area': np.concatenate([
...         np.random.normal(200, 30, 98),
...         [500, 50]  # outliers
...     ])
... })
>>> # Remove outliers
>>> detector = MADOutlierRemover(
...     on='Area',
...     groupby=['ImageName'],
...     threshold=3.5
... )
>>> filtered_data = detector.analyze(data)
>>> # Check results
>>> print(f"Original: {len(data)} rows, Filtered: {len(filtered_data)} rows")
>>> print(f"Removed {len(data) - len(filtered_data)} outliers")

Notes

Stores original data in self._original_data for visualization
Stores filtered results in self._latest_measurements for retrieval
Groups are processed independently with their own median and MAD
NaN values in measurement column are preserved in output

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

Visualize outlier detection results.

Creates a visualization showing the distribution of values with outliers highlighted and modified Z-score bounds displayed. Can display as individual subplots or as a collapsed stacked view with all groups in a single plot. Outlier flags are computed dynamically for visualization only.

Parameters:

figsize (tuple[int, int] | None) – Figure size as (width, height). If None, automatically determined based on number of groups and mode.
max_groups (int) – Maximum number of groups to display. If there are more groups, only the first max_groups will be shown. Default is 20.
collapsed (bool) – If True, show all groups stacked vertically in a single plot. If False, show each group in its own subplot. Default is True.
criteria (dict[str, Any] | None) – Optional dictionary specifying filtering criteria for data selection. When provided, only groups matching the criteria will be displayed. Format: {‘column_name’: value} or {‘column_name’: [value1, value2]}. Default is None (show all groups).
**kwargs – Additional matplotlib parameters to customize the plot. Common options include: - dpi: Figure resolution (default 100) - facecolor: Figure background color - edgecolor: Figure edge color - grid_alpha: Alpha value for grid lines (default 0.3) - grid_axis: Which axis to apply grid to (‘both’, ‘x’, ‘y’) - legend_loc: Legend location (default ‘best’) - legend_fontsize: Font size for legend (default 8)

Returns:

Tuple of (Figure, Axes) containing the visualization.

Raises:

ValueError – If analyze() has not been called yet (no results to display).
KeyError – If criteria references columns not present in the data.

Return type:

tuple[TypeAliasForwardRef(‘matplotlib.figure.Figure’), TypeAliasForwardRef(‘matplotlib.axes.Axes’)]

Examples

Visualize outlier detection with multiple grouping options:

>>> import pandas as pd
>>> import numpy as np
>>> from phenotypic.analysis import MADOutlierRemover
>>> # Create sample data with multiple grouping columns
>>> np.random.seed(42)
>>> data = pd.DataFrame({
...     'ImageName': ['img1', 'img2'] * 50,
...     'Plate': ['P1'] * 50 + ['P2'] * 50,
...     'Area': np.concatenate([
...         np.random.normal(200, 30, 48), [500, 550],
...         np.random.normal(180, 25, 48), [50, 600]
...     ])
... })
>>> # Remove outliers and visualize all groups
>>> detector = MADOutlierRemover(
...     on='Area',
...     groupby=['Plate', 'ImageName'],
...     threshold=3.5
... )
>>> results = detector.analyze(data)
>>> fig, axes = detector.show(figsize=(12, 5))
>>> # Visualize only specific plate
>>> fig, axes = detector.show(criteria={'Plate': 'P1'})

Notes

Individual mode (collapsed=False): - Each group gets its own subplot with box plot - Outliers shown in red, normal values in blue - Horizontal lines show the modified Z-score bounds

Collapsed mode (collapsed=True): - All groups stacked vertically in single plot - Each group shown as horizontal line with median marker - Vertical bars show the modified Z-score bounds - Normal points as circles, outliers as diamonds

Filtering with criteria: - Only groups matching all criteria are displayed - Useful for focusing on specific plates, conditions, or subsets

results() → pandas.DataFrame[source]#

Return the filtered results (outliers removed).

Returns the DataFrame with outliers removed from the most recent call to analyze().

Returns:: DataFrame with outliers filtered out. Contains only the original columns without additional outlier flag columns. If analyze() has not been called, returns an empty DataFrame.
Return type:: pandas.DataFrame

Examples

Retrieve filtered results after analysis:

>>> detector = MADOutlierRemover(
...     on='Area',
...     groupby=['ImageName']
... )
>>> filtered_data = detector.analyze(data)
>>> results_copy = detector.results()  # Same as filtered_data
>>> assert results_copy.equals(filtered_data)

Notes

Returns the DataFrame stored in self._latest_measurements
Contains only inliers (outliers have been removed)
Use this method to retrieve results after calling analyze()

__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

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=None), 'groupby': FieldInfo(annotation=list[str], required=True, description='List of column names to group by.', metadata=[_ColumnRefMarker('measurements')]), 'n_jobs': FieldInfo(annotation=int, required=False, default=1, alias_priority=2, validation_alias=AliasChoices(choices=['n_jobs', 'num_workers']), description='Number of parallel workers. Default is 1.'), 'on': FieldInfo(annotation=str, required=True, description='Column to test for outliers.', metadata=[_ColumnRefMarker('measurements')]), 'threshold': FieldInfo(annotation=float, required=False, default=3.5, description='Modified Z-score cutoff used for outlier identification.')}#

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

on: ColumnRef#

groupby: ColumnRefList#

n_jobs: int#

phenotypic.analysis.MADOutlierRemover#

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