phenotypic.analysis.abc_ package

Provides the base classes for set analysis operations.

This module imports and exposes the SetAnalyzer class from the internal _set_analyzer module. The SetAnalyzer class is designed for performing various analytical operations on sets. This is a utility module intended for use in applications that require set analysis functionality. Only the SetAnalyzer class is explicitly exposed as part of this module’s public API.

class phenotypic.analysis.abc_.ModelFitter(*, 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')] = 'Metadata_Time', loss: Literal['linear', 'soft_l1', 'huber', 'cauchy', 'arctan'] = 'huber', f_scale: Annotated[float, Gt(gt=0), _PydanticGeneralMetadata(allow_inf_nan=False)] = 1.0, verbose: bool = False)

Bases: SetAnalyzer, ABC

Template base class for grouped least-squares model fitting.

Subclasses provide the mathematical model (model_func), the loss function (_loss_func), an initial parameter guess, parameter bounds, and a small set of hooks that let the base class drive analyze, show, dash, and results without hard-coding parameter names.

Fit-quality metrics (MAE, MSE, RMSE, R²) and optimizer diagnostics (loss, status, sample count) are emitted under the shared MODEL_METRICS category so that every ModelFitter subclass produces a consistent set of diagnostic columns.

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')])

• loss (Literal['linear', 'soft_l1', 'huber', 'cauchy', 'arctan'])

• f_scale (float)

• verbose (bool)

time_label

Column name representing the independent variable (typically time).

Type:

str

loss

Loss calculation method passed through to scipy.optimize.least_squares(). Defaults to "huber" — quadratic near zero and linear past f_scale, so the fit behaves like standard least-squares on inliers but downweights rare large residuals (bubble artifacts, contamination spikes, mis-segmented timepoints). Pass "linear" to recover the classical unweighted-squared-residual loss, or "soft_l1" / "cauchy" / "arctan" for progressively more aggressive outlier suppression.

Type:

LossKind

f_scale

Soft margin between inlier and outlier residuals handed to scipy.optimize.least_squares(). Only affects robust loss choices; ignored when loss="linear". Must be positive and finite.

Type:

float

verbose

Whether to print detailed optimizer output.

Type:

bool

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

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

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

Return type:

Self

classmethod from_orm(obj: Any) → Self

Parameters:

obj (Any)

Return type:

Self

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

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

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

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

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

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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

abstractmethod static model_func(*args, **kwargs)

Mathematical model. First positional argument is the independent variable.

__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

__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]

__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

Fit the model to every group of data and return the results.

Standard template: copy, float-coerce the time column, aggregate to one sample per timepoint, dispatch per-group fits (serial or parallel via joblib.Parallel), concatenate, and append constant hyperparameter columns from _post_fit_columns.

Parameters:

data (pandas.DataFrame)

Return type:

pandas.DataFrame

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → 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(tmax: int | float | None = None, criteria: Dict[str, Any | List[Any]] | None = None, figsize=(6, 4), cmap: str | None = 'tab20', legend: bool | str = True, **kwargs) → go.Figure

Interactive Plotly version of show().

Hover tooltips are populated from _hover_fields so subclasses can expose whichever fitted parameters and metrics are most meaningful for their model.

Parameters:

• legend (bool | str) – Controls legend rendering. True (default) renders the legend with one entry per groupby combination (joined with ", "). False hides the legend. A string must be one of self.groupby; groups sharing a value in that column share both color and a single legend entry.

• tmax (int | float | None)

• criteria (Dict[str, Union[Any, List[Any]]] | None)

• cmap (str | None)

Raises:

ImportError – If plotly is not installed.

Return type:

go.Figure

dict(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

Parameters:

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

Return type:

Dict[str, Any]

json(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

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_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

model_dump(*, mode: Literal['json', 'python'] | str = 'python', 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → dict[str, Any]

!!! 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]

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → str

!!! 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

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

results() → pandas.DataFrame

Return the most recent fit results produced by analyze().

Return type:

pandas.DataFrame

show(tmax: int | float | None = None, criteria: Dict[str, Any | List[Any]] | None = None, figsize=(6, 4), cmap: str | None = 'tab20', legend: bool | str = True, ax: plt.Axes | None = None, **kwargs) → Tuple[plt.Figure, plt.Axes]

Plot model predictions alongside measurements with optional filtering.

Parameters:

• tmax (int | float | None) – Upper bound of the prediction curve. If None, uses the maximum observed time.

• criteria (Dict[str, Union[Any, List[Any]]] | None) – Column/value filter applied to both fitted results and raw measurements before plotting.

• figsize – Matplotlib figure size. Used only when ax is None.

• cmap (str | None) – Matplotlib colormap name, a single color string, or None for matplotlib’s default color cycle.

• legend (bool | str) – Controls legend rendering. True (default) renders the legend with one entry per groupby combination, labeled by the first groupby column. False hides the legend. A string must be one of self.groupby; groups sharing a value in that column share both color and a single legend entry. The legend is auto-removed if it is larger than the axes.

• ax (plt.Axes | None) – Existing axes to draw into. A new figure is created when omitted.

• **kwargs – Styling overrides — dpi, facecolor, edgecolor, line_width, marker_size, elinewidth, capsize, legend_loc, legend_fontsize, label.

Returns:

A (Figure, Axes) pair.

Return type:

Tuple[plt.Figure, plt.Axes]

agg_func: Callable | str | list | dict | None

f_scale: float

groupby: ColumnRefList

loss: LossKind

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].

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'), 'f_scale': FieldInfo(annotation=float, required=False, default=1.0, description='Soft margin between inlier and outlier residuals handed to :func:`scipy.optimize.least_squares`. Only affects robust ``loss`` choices; ignored when ``loss="linear"``. Must be positive and finite.', metadata=[Gt(gt=0), _PydanticGeneralMetadata(allow_inf_nan=False)]), 'groupby': FieldInfo(annotation=list[str], required=True, metadata=[_ColumnRefMarker('measurements')]), 'loss': FieldInfo(annotation=Literal['linear', 'soft_l1', 'huber', 'cauchy', 'arctan'], required=False, default='huber', description='Loss calculation method passed through to :func:`scipy.optimize.least_squares`. Defaults to ``"huber"`` — quadratic near zero and linear past ``f_scale``, so the fit behaves like standard least-squares on inliers but downweights rare large residuals (bubble artifacts, contamination spikes, mis-segmented timepoints). Pass ``"linear"`` to recover the classical unweighted-squared-residual loss, or ``"soft_l1"`` / ``"cauchy"`` / ``"arctan"`` for progressively more aggressive outlier suppression.'), 'n_jobs': FieldInfo(annotation=int, required=False, default=1, alias_priority=2, validation_alias=AliasChoices(choices=['n_jobs', 'num_workers'])), 'on': FieldInfo(annotation=str, required=True, metadata=[_ColumnRefMarker('measurements')]), 'time_label': FieldInfo(annotation=str, required=False, default='Metadata_Time', description='Column name representing the independent variable (typically time).', metadata=[_ColumnRefMarker('measurements')]), 'verbose': FieldInfo(annotation=bool, required=False, default=False, description='Whether to print detailed optimizer output.')}

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.

n_jobs: int

on: ColumnRef

time_label: ColumnRef

verbose: bool

class phenotypic.analysis.abc_.QualityCheck(*, on: ~typing.Annotated[str, _ColumnRefMarker('measurements')], groupby: ~typing.Annotated[list[str], _ColumnRefMarker('measurements')], agg_func: ~typing.Callable | str | list | dict | None = 'mean', n_jobs: int = 1, warn_threshold: float = 0.05, fail_threshold: float = 0.1, unmatched_groups: list = <factory>)

Bases: SetAnalyzer, ABC

Detect quality-control issues in measurement frames.

QualityCheck is a thin layer over SetAnalyzer that standardizes how subclasses surface flagged rows for downstream curation. Subclasses implement a single _compute(group) hook that augments one group with at minimum a raw, directional QC_<name>_Metric column (the check’s headline value in its own units). The base class then derives two companion columns from the metric, using the check’s _HIGHER_IS_BAD direction flag to decide which side of each threshold is “bad”:

• QC_<name>_Flag (bool): True when the metric crosses fail_threshold in the bad direction. When _HIGHER_IS_BAD is True that means metric >= fail_threshold; when False (e.g. an agreement score where lower is worse) it means metric <= fail_threshold. Rows with Flag=True are the ones the results-viewer GUI offers to mark for curation removal.

• QC_<name>_Status (str): tri-state label derived from the same metric column. With _HIGHER_IS_BAD=True: "pass" until metric >= warn_threshold ("warn"), then metric >= fail_threshold ("fail"), with warn_threshold <= fail_threshold. With _HIGHER_IS_BAD=False the comparisons invert to <= with fail_threshold <= warn_threshold. Only "fail" triggers Flag=True; the "warn" tier is informational.

NaN metrics (e.g. under-powered replicate bins in ReplicateAgreement) are treated as "pass" with Flag=False so degenerate groups never gate curation.

Subclasses set class-level attributes that drive column naming, threshold direction, and docstring autogeneration:

• name — short identifier composed into output column names (QC_<name>_Flag and friends). Must be set on every concrete subclass.

• _HIGHER_IS_BAD — True when a larger metric value is worse (the common case), False when a smaller value is worse (e.g. an agreement score). Intrinsic to the metric, not user-tunable; it has no default on the base and must be set on every concrete subclass.

• _measurement_infoclass — optional per-subclass MeasurementInfo enum documenting any check-specific columns the subclass emits beyond the generic Flag/Metric/Status trio. When set, __init_subclass__ appends its RST table to the subclass docstring.

The base class drives group iteration directly in analyze(), so the abstract SetAnalyzer._apply2group_func() is overridden to raise — subclasses implement _compute instead.

Parameters:

• on (Annotated[str, _ColumnRefMarker('measurements')])

• groupby (Annotated[list[str], _ColumnRefMarker('measurements')])

• agg_func (Callable | str | list | dict | None)

• n_jobs (int)

• warn_threshold (float)

• fail_threshold (float)

• unmatched_groups (list)

name

Short identifier composed into output column names. Set on each concrete subclass (e.g. "Count", "SE").

Type:

ClassVar[str]

warn_threshold

Metric value (in the check’s own units) at which Status becomes "warn" in the bad direction. A pydantic instance field; subclasses override the default by re-declaring it, and callers may override it per instance.

Type:

float

fail_threshold

Metric value at which Status becomes "fail" and Flag=True in the bad direction. A pydantic instance field; subclasses override the default by re-declaring it, and callers may override it per instance.

Type:

float

unmatched_groups

Groups that the check could not evaluate (for example, expected counts whose group key never appeared in the data). Populated by subclasses that need to report missing combinations; empty by default.

Type:

list

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

classmethod __init_subclass__(**kwargs: Any) → None

Append QC and per-check RST tables to the subclass docstring.

Skips intermediate ABCs that have not yet bound name. When the subclass declares both a docstring and a name, the generic QUALITY_CHECK table is appended (substituting name into the column headers). If _measurement_infoclass is also set, its table is appended as well so check-specific columns are documented alongside the generic trio.

Parameters:

kwargs (Any)

Return type:

None

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

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

Return type:

Self

classmethod flag_col() → str

Return the flag column name for this check.

Return type:

str

classmethod from_orm(obj: Any) → Self

Parameters:

obj (Any)

Return type:

Self

classmethod metric_col() → str

Return the metric column name for this check.

Return type:

str

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

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

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

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

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

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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 status_col() → str

Return the status column name for this check.

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

__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

__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]

__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

Run the check on every group and return the augmented frame.

Iterates over data.groupby(self.groupby, dropna=False), delegates per-group computation to _compute(), and adds three generic columns derived from the metric:

• QC_<name>_Metric (carry-through from _compute)

• QC_<name>_Flag (bool)

• QC_<name>_Status ("pass" / "warn" / "fail")

Flag and Status are directional. With _HIGHER_IS_BAD=True a row fails when metric >= fail_threshold and warns when metric >= warn_threshold; with _HIGHER_IS_BAD=False the comparisons invert to <=. A NaN metric always yields Status="pass" and Flag=False.

Rows are never dropped. The augmented frame is stored on _latest_measurements and returned.

Parameters:

data (pandas.DataFrame) – Input measurement frame. Must contain self.on and every column in self.groupby.

Returns:

The input frame with the three generic QC columns appended plus whatever _compute contributed.

Raises:

KeyError – If self.on or any column in self.groupby is missing from data.

Return type:

pandas.DataFrame

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → 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.

dict(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

Parameters:

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

Return type:

Dict[str, Any]

flagged_keys() → list[tuple[str, int]]

Return (Metadata_ImageFile, Object_Label) pairs to curate.

Used by the GUI “Mark all flagged for removal” button. Requires the analyzed frame to carry both Metadata_ImageFile and Object_Label columns (the curation key used by STORE_REMOVED_KEYS). Returns an empty list when those columns are absent or when no rows were flagged.

Returns:

De-duplicated list of (image_file, object_label) tuples for rows where Flag=True.

Return type:

list[tuple[str, int]]

group_members() → dict[tuple, list[tuple[str, int, Any]]]

Map each group key to its member rows for worklists/galleries.

Walks the most recent analyzed frame and, for every group key produced by data.groupby(self.groupby, dropna=False), collects the rows that belong to it as (Metadata_ImageFile, Object_Label, member_value) tuples, where member_value is the row’s self.on value (the column the check operates on). The mapping preserves group iteration order.

Mirrors flagged_keys()’s guard: if the analyzed frame lacks either Metadata_ImageFile or the object-label column, an empty mapping is returned rather than raising.

Returns:

Ordered mapping of group key (always a tuple, even for a single groupby column) to a list of (image_file, object_label, member_value) tuples. Empty when the curation key columns are absent.

Return type:

dict[tuple, list[tuple[str, int, Any]]]

json(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

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_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

model_dump(*, mode: Literal['json', 'python'] | str = 'python', 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → dict[str, Any]

!!! 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]

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → str

!!! 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

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

results() → pandas.DataFrame

Return the augmented frame stored by the most recent analyze().

Return type:

pandas.DataFrame

show(*args: Any, **kwargs: Any) → Any

QualityCheck plots are Plotly-only — see dash().

SetAnalyzer’s matplotlib show() is not implemented for QC because the QC tab is Plotly-driven. Raising rather than falling back to a placeholder so notebook users discover the right method.

Raises:

NotImplementedError – Always; use dash() instead.

Parameters:

• args (Any)

• kwargs (Any)

Return type:

Any

summary() → pandas.DataFrame

Return a one-row-per-group summary of the most recent analyze.

The aggregate columns are prefixed with ``qc_`` so they can never collide with a groupby column on reset_index — a plate-layout column literally named status or num_rows would otherwise raise. The summary therefore always carries the group key columns plus the four prefixed aggregates.

Returns:

DataFrame with columns [*self.groupby, "qc_n_members", "qc_n_flagged", "qc_worst_metric", "qc_status"]. qc_worst_metric is the extreme metric value in the bad direction across the group: group[metric_col].max() when _HIGHER_IS_BAD is True, else group[metric_col].min(). qc_status is the worst status across the group: "fail" wins over "warn" which wins over "pass".

Return type:

pandas.DataFrame

agg_func: Callable | str | list | dict | None

fail_threshold: float

groupby: ColumnRefList

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].

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'), 'fail_threshold': FieldInfo(annotation=float, required=False, default=0.1, description='Metric value at which ``Status`` becomes ``"fail"`` and ``Flag=True`` in the bad direction. A pydantic instance field; subclasses override the default by re-declaring it, and callers may override it per instance.'), 'groupby': FieldInfo(annotation=list[str], required=True, metadata=[_ColumnRefMarker('measurements')]), 'n_jobs': FieldInfo(annotation=int, required=False, default=1, alias_priority=2, validation_alias=AliasChoices(choices=['n_jobs', 'num_workers'])), 'on': FieldInfo(annotation=str, required=True, metadata=[_ColumnRefMarker('measurements')]), 'unmatched_groups': FieldInfo(annotation=list, required=False, default_factory=list, description='Groups that the check could not evaluate (for example, expected counts whose group key never appeared in the data). Populated by subclasses that need to report missing combinations; empty by default.'), 'warn_threshold': FieldInfo(annotation=float, required=False, default=0.05, description='Metric value (in the check\'s own units) at which ``Status`` becomes ``"warn"`` in the bad direction. A pydantic instance field; subclasses override the default by re-declaring it, and callers may override it per instance.')}

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.

n_jobs: int

name: ClassVar[str]

on: ColumnRef

unmatched_groups: list

warn_threshold: float

class phenotypic.analysis.abc_.SetAnalyzer(*, on: Annotated[str, _ColumnRefMarker('measurements')], groupby: Annotated[list[str], _ColumnRefMarker('measurements')], agg_func: Callable | str | list | dict | None = 'mean', n_jobs: int = 1)

Bases: BaseModel, ABC

Abstract base for grouped analyses over a measurement DataFrame.

SetAnalyzer is the root of PhenoTypic’s analyzer hierarchy. It is a separate pydantic BaseModel root — it is not a BaseOperation. Subclasses operate on an already-assembled measurement DataFrame via analyze() (analyzers use .analyze(), not .apply()).

Analyzer parameters are declared as annotated class-level fields; pydantic generates the constructor and validates inputs.

Parameters:

• on (Annotated[str, _ColumnRefMarker('measurements')]) – Measurement column the analysis operates on.

• groupby (Annotated[list[str], _ColumnRefMarker('measurements')]) – Columns that define the per-group iteration unit.

• agg_func (Callable | str | list | dict | None) – Aggregation applied within each group. A Callable is accepted at runtime but cannot round-trip through JSON; only the str/list/dict forms serialize losslessly. Defaults to "mean".

• n_jobs (int) – Worker count for parallel group processing. The legacy num_workers keyword is accepted as an alias. Defaults to 1.

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

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

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

Return type:

Self

classmethod from_orm(obj: Any) → Self

Parameters:

obj (Any)

Return type:

Self

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

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

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

classmethod model_validate(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, from_attributes: bool | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → Self

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

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, extra: Literal['allow', 'ignore', 'forbid'] | None = None, context: Any | None = None, by_alias: bool | None = None, by_name: bool | None = None) → 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

__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

__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]

__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

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

Parameters:

data (pandas.DataFrame)

Return type:

pandas.DataFrame

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → 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.

dict(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]

Parameters:

• include (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

Return type:

Dict[str, Any]

json(*, 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 = 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 = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str

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_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

model_dump(*, mode: Literal['json', 'python'] | str = 'python', 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → dict[str, Any]

!!! 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]

model_dump_json(*, indent: int | None = None, ensure_ascii: bool = False, 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 = 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 = None, context: Any | None = None, by_alias: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, exclude_computed_fields: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, fallback: Callable[[Any], Any] | None = None, serialize_as_any: bool = False) → str

!!! 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

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

abstractmethod results()

abstractmethod show()

agg_func: Callable | str | list | dict | None

groupby: ColumnRefList

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].

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'), 'groupby': FieldInfo(annotation=list[str], required=True, metadata=[_ColumnRefMarker('measurements')]), 'n_jobs': FieldInfo(annotation=int, required=False, default=1, alias_priority=2, validation_alias=AliasChoices(choices=['n_jobs', 'num_workers'])), 'on': FieldInfo(annotation=str, required=True, metadata=[_ColumnRefMarker('measurements')])}

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.

n_jobs: int

on: ColumnRef