napari.components.Dims

class napari.components.Dims(*, ndim: int = 2, ndisplay: Literal[2, 3] = 2, order: tuple[int, ...] = (), axis_labels: tuple[str, ...] = (), rollable: tuple[bool, ...] = (), range: tuple[RangeTuple, ...] = (), margin_left: tuple[float, ...] = (), margin_right: tuple[float, ...] = (), point: tuple[float, ...] = (), last_used: int = 0)

Bases: EventedModel

Dimensions object modeling slicing and displaying.

Parameters:

• ndim (int) – Number of dimensions.

• ndisplay (int) – Number of displayed dimensions.

• range (tuple of 3-tuple of float) – List of tuples (min, max, step), one for each dimension in world coordinates space. Lower and upper bounds are inclusive.

• point (tuple of floats) – Dims position in world coordinates for each dimension.

• margin_left (tuple of floats) – Left margin in world pixels of the slice for each dimension.

• margin_right (tuple of floats) – Right margin in world pixels of the slice for each dimension.

• order (tuple of int) – Tuple of ordering the dimensions, where the last dimensions are rendered.

• axis_labels (tuple of str) – Tuple of labels for each dimension.

• last_used (int) – Dimension which was last interacted with.

ndim

Number of dimensions.

Type:

int

ndisplay

Number of displayed dimensions.

Type:

int

range

List of tuples (min, max, step), one for each dimension in world coordinates space. Lower and upper bounds are inclusive.

Type:

tuple of 3-tuple of float

point

Dims position in world coordinates for each dimension.

Type:

tuple of floats

margin_left

Left margin (=thickness) in world pixels of the slice for each dimension.

Type:

tuple of floats

margin_right

Right margin (=thickness) in world pixels of the slice for each dimension.

Type:

tuple of floats

order

Tuple of ordering the dimensions, where the last dimensions are rendered.

Type:

tuple of int

axis_labels

Tuple of labels for each dimension.

Type:

tuple of str

last_used

Dimension which was last used. Tuple the slider position for each dims slider, in world coordinates.

Type:

int

current_step

Current step for each dimension (same as point, but in slider coordinates).

Type:

tuple of int

nsteps

Number of steps available to each slider. These are calculated from the range.

Type:

tuple of int

thickness

Thickness of the slice (sum of both margins) for each dimension in world coordinates.

Type:

tuple of floats

displayed

List of dimensions that are displayed. These are calculated from the order and ndisplay.

Type:

tuple of int

not_displayed

List of dimensions that are not displayed. These are calculated from the order and ndisplay.

Type:

tuple of int

displayed_order

Order of only displayed dimensions. These are calculated from the displayed dimensions.

Type:

tuple of int

rollable

Tuple of axis roll state. If True the axis is rollable.

Type:

tuple of bool

Methods

construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
enums_as_values([as_values])	Temporarily override how enums are retrieved.
from_orm(obj)
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
model_json_schema(*args, **kwargs)	Generate a JSON schema for this model.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
reset()	Reset dims values to initial states.
roll()	Roll order of dimensions for display.
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
set_axis_label(axis, label)	Sets new axis labels for the given axes.
set_current_step(axis, value)
set_point(axis, value)	Sets point to slice dimension in world coordinates.
set_range(axis, _range)	Sets ranges (min, max, step) for the given dimensions.
transpose()	Transpose displayed dimensions.
update(values[, recurse])	Update a model in place.
update_forward_refs(**localns)
validate(value)

Attributes

current_step
displayed	Dimensions that are displayed.
displayed_order
events
margin_left_step
margin_right_step
model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
not_displayed	Dimensions that are not displayed.
nsteps
thickness
thickness_step

Details

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 – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – 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.

property displayed: tuple[int, ...]

Dimensions that are displayed.

Type:

Tuple

enums_as_values(as_values: bool = True)

Temporarily override how enums are retrieved.

Parameters:

as_values (bool, optional) – Whether enums should be shown as values (or as enum objects), by default True

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'json_encoders': {<class 'app_model.types._keys._keybindings.KeyBinding'>: <function <lambda>>, <class 'napari.layers.utils.color_encoding.ColorEncoding'>: <function StyleEncoding._json_encode>, <class 'napari.layers.utils.string_encoding.StringEncoding'>: <function StyleEncoding._json_encode>, <class 'napari.settings._fields.Version'>: <function Version._json_encode>, <class 'numpy.ndarray'>: <function <lambda>>}, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

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

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set – 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 – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

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 – 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 – Set to True to make a deep copy of the model.

Returns:

New model instance.

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 – 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 – A set of fields to include in the output.

• exclude – A set of fields to exclude from the output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – 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 – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

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 – Indentation to use in the JSON output. If None is passed, the output will be compact.

• ensure_ascii – 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 – Field(s) to include in the JSON output.

• exclude – Field(s) to exclude from the JSON output.

• context – Additional context to pass to the serializer.

• by_alias – Whether to serialize using field aliases.

• exclude_unset – Whether to exclude fields that have not been explicitly set.

• exclude_defaults – Whether to exclude fields that are set to their default value.

• exclude_none – Whether to exclude fields that have a value of None.

• exclude_computed_fields – 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 – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

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”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(*args, **kwargs) → dict[str, Any]

Generate a JSON schema for this model.

This is required to prevent from mail formated docstring break docs build.

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 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.

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 – The BaseModel instance.

• context – The context.

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 – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors – Whether to raise errors, defaults to True.

• _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

• _types_namespace – 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.

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 – The object to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• from_attributes – Whether to extract data from object attributes.

• context – Additional context to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – 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.

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 – The JSON data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – 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.

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 – The object containing string data to validate.

• strict – Whether to enforce types strictly.

• extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

• context – Extra variables to pass to the validator.

• by_alias – Whether to use the field’s alias when validating against the provided input data.

• by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

property not_displayed: tuple[int, ...]

Dimensions that are not displayed.

Type:

Tuple

reset()

Reset dims values to initial states.

roll()

Roll order of dimensions for display.

set_axis_label(axis: int | Sequence[int], label: str | Sequence[str])

Sets new axis labels for the given axes.

Parameters:

• axis (int or sequence of int) – Dimension index or a sequence of axes whos labels will be set.

• label (str or sequence of str) – Given labels for the specified axes.

set_point(axis: int | Sequence[int], value: float | Sequence[float])

Sets point to slice dimension in world coordinates.

Parameters:

• axis (int or sequence of int) – Dimension index or a sequence of axes whos point will be set.

• value (scalar or sequence of scalars) – Value of the point for each axis.

set_range(axis: int | Sequence[int], _range: Sequence[int | float] | Sequence[Sequence[int | float]])

Sets ranges (min, max, step) for the given dimensions.

Parameters:

• axis (int or sequence of int) – Dimension index or a sequence of axes whos range will be set.

• _range (tuple or sequence of tuple) – Range specified as (min, max, step) or a sequence of these range tuples.

transpose()

Transpose displayed dimensions.

This swaps the order of the last two displayed dimensions. The order of the displayed is taken from Dims.order.

update(values: EventedModel | dict, recurse: bool = True) → None

Update a model in place.

Parameters:

• values (dict, napari.utils.events.EventedModel) – Values to update the model with. If an EventedModel is passed it is first converted to a dictionary. The keys of this dictionary must be found as attributes on the current model.

• recurse (bool) – If True, recursively update fields that are EventedModels. Otherwise, just update the immediate fields of this EventedModel, which is useful when the declared field type (e.g. Union) can have different realized types with different fields.