Tiled
Image tiling module
- class PixelValueModel(*, values: list[float | None])[source]
Bases:
BaseModel
Pydantic model class for VisiOmatic pixel values.
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Model
Returns a copy of the model.
- !!! warning "Deprecated"
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py 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.
- model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- model_config: ClassVar[ConfigDict] = {}
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) Model
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 -- The set of field names accepted for the Model instance.
values -- Trusted or pre-validated data dictionary.
- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
- 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] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
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.
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].
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, include: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
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.
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.
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].
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".
- model_fields: ClassVar[dict[str, FieldInfo]] = {'values': FieldInfo(annotation=list[Union[float, NoneType]], required=True)}
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
This replaces Model.__fields__ from Pydantic V1.
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') dict[str, Any]
Generates a JSON schema for a model class.
- Parameters:
by_alias -- Whether to use attribute aliases or not.
ref_template -- The reference template.
schema_generator -- To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode -- The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- 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(_BaseModel__context: Any) None
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | 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, from_attributes: bool | None = None, context: dict[str, Any] | None = None) Model
Validate a pydantic model instance.
- Parameters:
obj -- The object to validate.
strict -- Whether to enforce types strictly.
from_attributes -- Whether to extract data from object attributes.
context -- Additional context to pass to the validator.
- 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, context: dict[str, Any] | None = None) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#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.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValueError -- If json_data is not a JSON string.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) Model
Validate the given object contains string data against the Pydantic model.
- Parameters:
obj -- The object contains string data to validate.
strict -- Whether to enforce types strictly.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- class PixelModel(*, x: int, y: int, values: list[float | None])[source]
Bases:
BaseModel
Pydantic model class for pixels.
- Parameters:
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Model
Returns a copy of the model.
- !!! warning "Deprecated"
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py 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.
- model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- model_config: ClassVar[ConfigDict] = {}
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) Model
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 -- The set of field names accepted for the Model instance.
values -- Trusted or pre-validated data dictionary.
- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
- 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] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
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.
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].
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, include: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
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.
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.
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].
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".
- model_fields: ClassVar[dict[str, FieldInfo]] = {'values': FieldInfo(annotation=list[Union[float, NoneType]], required=True), 'x': FieldInfo(annotation=int, required=True), 'y': FieldInfo(annotation=int, required=True)}
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
This replaces Model.__fields__ from Pydantic V1.
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') dict[str, Any]
Generates a JSON schema for a model class.
- Parameters:
by_alias -- Whether to use attribute aliases or not.
ref_template -- The reference template.
schema_generator -- To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode -- The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- 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(_BaseModel__context: Any) None
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | 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, from_attributes: bool | None = None, context: dict[str, Any] | None = None) Model
Validate a pydantic model instance.
- Parameters:
obj -- The object to validate.
strict -- Whether to enforce types strictly.
from_attributes -- Whether to extract data from object attributes.
context -- Additional context to pass to the validator.
- 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, context: dict[str, Any] | None = None) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#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.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValueError -- If json_data is not a JSON string.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) Model
Validate the given object contains string data against the Pydantic model.
- Parameters:
obj -- The object contains string data to validate.
strict -- Whether to enforce types strictly.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- class ProfileModel(*, profile: list[PixelModel])[source]
Bases:
BaseModel
Pydantic model class for VisiOmatic profiles.
- Parameters:
profile (list[PixelModel]) -- List of pixel models.
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Model
Returns a copy of the model.
- !!! warning "Deprecated"
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py 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.
- model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- model_config: ClassVar[ConfigDict] = {}
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) Model
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 -- The set of field names accepted for the Model instance.
values -- Trusted or pre-validated data dictionary.
- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
- 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] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
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.
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].
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, include: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
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.
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.
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].
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".
- model_fields: ClassVar[dict[str, FieldInfo]] = {'profile': FieldInfo(annotation=list[PixelModel], required=True)}
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
This replaces Model.__fields__ from Pydantic V1.
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') dict[str, Any]
Generates a JSON schema for a model class.
- Parameters:
by_alias -- Whether to use attribute aliases or not.
ref_template -- The reference template.
schema_generator -- To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode -- The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- 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(_BaseModel__context: Any) None
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | 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, from_attributes: bool | None = None, context: dict[str, Any] | None = None) Model
Validate a pydantic model instance.
- Parameters:
obj -- The object to validate.
strict -- Whether to enforce types strictly.
from_attributes -- Whether to extract data from object attributes.
context -- Additional context to pass to the validator.
- 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, context: dict[str, Any] | None = None) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#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.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValueError -- If json_data is not a JSON string.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) Model
Validate the given object contains string data against the Pydantic model.
- Parameters:
obj -- The object contains string data to validate.
strict -- Whether to enforce types strictly.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- class TiledModel(*, type: str, version: str, image_name: str, object_name: str, full_size: Tuple[int, ...], tile_size: Tuple[int, ...], tile_levels: int, channels: int, bits_per_channel: int, brightness: float, contrast: float, color_saturation: float, gamma: float, quality: int, header: dict, images: List[ImageModel])[source]
Bases:
BaseModel
Pydantic tiled model class.
- Parameters:
type (str) -- Name of the web service.
version (str) -- Version of the web service.
full_size (List[int]) -- Full raster size, FITS style (x comes first).
tile_size (List[int]) -- Tile size, FITS style.
tile_levels (int) -- Number of levels in the image pyramid.
channels (int) -- Number of channels.
bits_per_channel (int) -- Number of bits per pixel.
brightness (float) -- Relative tile brightness (black level).
contrast (float) -- Relative tile contrast.
color_saturation (float) -- Tile color saturation.
gamma (float) -- Tile display gamma.
quality (int) -- JPEG quality (0-100).
header (dict) -- Image header keyword/value pairs.
images (List[ImageModel]) -- List of image model objects.
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Model
Returns a copy of the model.
- !!! warning "Deprecated"
This method is now deprecated; use model_copy instead.
If you need include or exclude, use:
`py 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.
- model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
- model_config: ClassVar[ConfigDict] = {}
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) Model
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 -- The set of field names accepted for the Model instance.
values -- Trusted or pre-validated data dictionary.
- Returns:
A new instance of the Model class with validated data.
- model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#model_copy
Returns a copy of the model.
- 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] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump
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.
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].
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, include: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, exclude: Set[int] | Set[str] | Dict[int, Any] | Dict[str, Any] | None = None, context: dict[str, Any] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str
Usage docs: https://docs.pydantic.dev/2.7/concepts/serialization/#modelmodel_dump_json
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.
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.
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].
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".
- model_fields: ClassVar[dict[str, FieldInfo]] = {'bits_per_channel': FieldInfo(annotation=int, required=True), 'brightness': FieldInfo(annotation=float, required=True), 'channels': FieldInfo(annotation=int, required=True), 'color_saturation': FieldInfo(annotation=float, required=True), 'contrast': FieldInfo(annotation=float, required=True), 'full_size': FieldInfo(annotation=Tuple[int, ...], required=True), 'gamma': FieldInfo(annotation=float, required=True), 'header': FieldInfo(annotation=dict, required=True), 'image_name': FieldInfo(annotation=str, required=True), 'images': FieldInfo(annotation=List[ImageModel], required=True), 'object_name': FieldInfo(annotation=str, required=True), 'quality': FieldInfo(annotation=int, required=True), 'tile_levels': FieldInfo(annotation=int, required=True), 'tile_size': FieldInfo(annotation=Tuple[int, ...], required=True), 'type': FieldInfo(annotation=str, required=True), 'version': FieldInfo(annotation=str, required=True)}
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
This replaces Model.__fields__ from Pydantic V1.
- property model_fields_set: set[str]
Returns the set of fields that have been explicitly set on this model instance.
- Returns:
- A set of strings representing the fields that have been set,
i.e. that were not filled from defaults.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') dict[str, Any]
Generates a JSON schema for a model class.
- Parameters:
by_alias -- Whether to use attribute aliases or not.
ref_template -- The reference template.
schema_generator -- To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode -- The mode in which to generate the schema.
- Returns:
The JSON schema for the given model class.
- 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(_BaseModel__context: Any) None
Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.
- classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | 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, from_attributes: bool | None = None, context: dict[str, Any] | None = None) Model
Validate a pydantic model instance.
- Parameters:
obj -- The object to validate.
strict -- Whether to enforce types strictly.
from_attributes -- Whether to extract data from object attributes.
context -- Additional context to pass to the validator.
- 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, context: dict[str, Any] | None = None) Model
Usage docs: https://docs.pydantic.dev/2.7/concepts/json/#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.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- Raises:
ValueError -- If json_data is not a JSON string.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) Model
Validate the given object contains string data against the Pydantic model.
- Parameters:
obj -- The object contains string data to validate.
strict -- Whether to enforce types strictly.
context -- Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- class Tiled(filename: str, extnum: int | None = None, tilesize: Tuple[int, int] = (256, 256), minmax: Tuple[int, int] | None = None, brightness: float | None = None, contrast: float | None = None, color_saturation: float | None = None, gamma: float | None = None, quality: int | None = None, max_region_tile_count: int | None = None, nthreads: int | None = None)[source]
Bases:
object
Class for the tiled image pyramid to be visualized.
- Parameters:
extnum (int, optional) -- Extension number (for Multi-Extension FITS files).
tilesize (tuple[int, int], optional) -- shape of the served tiles.
minmax (tuple[float, float], optional) -- Intensity cuts of the served tiles.
brightness (float, optional) -- Relative tile black level of the served tiles.
contrast (float, optional) -- Relative tile contrast of the served tiles.
color_saturation (float, optional) -- Default color saturation of the served tiles.
gamma (float, optional) -- Display gamma of the served tiles.
nthreads (int, optional) -- Number of compute threads for parallelized operations.
- compute_nlevels() int [source]
Return the number of image resolution levels.
- Returns:
nlevels (int) -- Number of image resolution levels in the pyramid.
- compute_grid_shape(level: int = 0) Tuple[int, int, int] [source]
Return the number of tiles per axis at a given image resolution level.
- Returns:
shape (tuple[int, int, int]) -- Number of tiles.
- compute_tile_bordershape(level=0) Tuple[int, int, int] [source]
Return the border shape of tiles at a given image resolution level.
- Returns:
shape (tuple[int, int, int]) -- Border shape.
- get_model() TiledModel [source]
Return a Pydantic model of the tiled object.
- Returns:
model (TiledModel) -- Pydantic model instance of the tiled object
- make_header() <module 'astropy.io.fits.header' from '/home/bertin/.local/lib/python3.11/site-packages/astropy/io/fits/header.py'> [source]
Generate a FITS header with a global WCS for the mosaic.
- Returns:
header (~astropy.io.fits.Header) -- FITS header for the mosaic.
- get_iipheaderstr() str [source]
Generate an IIP image header.
- Returns:
header (str) -- IIP image header.
- convert_tile(tile: ndarray, channel: int | None = None, minmax: Tuple[Tuple[int, float, float], ...] | None = None, mix: Tuple[Tuple[int, float, float, float], ...] | None = None, brightness: float | None = None, contrast: float | None = None, gamma: float | None = None, colormap: str = 'grey', invert: bool = False) ndarray [source]
Process the dynamic range of a tile.
- Parameters:
tile (ndarray) -- Input tile.
channel (int, optional) -- Image channel
minmax (list[float, float], optional) -- Tile intensity cuts.
mix (list[int, float, float, float], optional) -- Tile slice RGB colors.
brightness (float, optional) -- Relative tile brightness (black level).
contrast (float, optional) -- Relative tile contrast.
gamma (float, optional) -- Inverse tile display gamma.
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
invert (bool, optional) -- Invert the colormap.
- Returns:
raster (~numpy.ndarray) -- Processed tile image raster.
- get_tile_raster(tilelevel: int, tileindex: int, channel: int | None = None, minmax: Tuple[Tuple[int, float, float], ...] | None = None, mix: Tuple[Tuple[int, float, float, float], ...] | None = None, brightness: float | None = None, contrast: float | None = None, gamma: float | None = None, colormap: str = 'grey', invert: bool = False, **_: Any) ndarray [source]
Compute a gray-level or color image raster from a tile.
- Parameters:
tilelevel (int) -- Tile resolution level.
tileindex (int) -- Tile index.
channel (int) -- Data channel (first channel is 1)
minmax (list[float, float], optional) -- Tile intensity cuts.
brightness (float, optional) -- Relative tile brightness.
contrast (float, optional) -- Relative tile contrast.
gamma (float, optional) -- Inverse tile display gamma.
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
invert (bool, optional) -- Invert the colormap.
- Returns:
raster (~numpy.ndarray) -- The computed tile image raster.
- encode(raster: ndarray, channel: int | None = None, colormap: str = 'grey', quality: int | None = None, **_: Any) bytes [source]
Generate a JPEG bytestream from an image raster (e.g., a tile).
- get_encoded_region(bounds: Tuple[Tuple[int, int], Tuple[int, int]], binning: int = 1, channel: int | None = None, colormap: str = 'grey', **kwargs: Any) bytes [source]
Return a JPEG bytestream of a specific image region by stitching tiles that fall in that region.
- Parameters:
bounds (tuple[tuple[int, int], tuple[int, int]]) -- Image boundaries in pixels.
binning (int, optional) -- Binning factor per axis, in pixels.
channel (int, optional) -- Data channel (first channel is 1)
colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.
**kwargs -- Additional get_tile_raster() and encode() keyword arguments.
- Returns:
tile (bytes) -- JPEG bytestream of the tile.
- Raises:
IndexError -- exception: An error occurred because of unexpected bounding box coordinates. It is raised if any of the following occur: - The number of requested region tiles exceeds max_region_tile_count. - Bounding box coordinates are inconsistent.
- get_pixel_values(channels: Tuple[int], pos: Tuple[int, int]) PixelValueModel [source]
Get pixel values at the given pixel coordinates in merged frame.
- get_profiles(channels: Tuple[int, ...] | None, pos1: Tuple[int, int], pos2: Tuple[int, int]) ProfileModel [source]
Get image profile(s) between the given pixel coordinates in the merged frame.
- Parameters:
- Returns:
profile (ProfileModel) -- Profile pydantic model of pixel value(s) along the line.
- pickledTiled(filename: str, **kwargs) Tiled [source]
Return pickled Tiled object if available, or initialized otherwise.
- get_object_filename(image_filename: str) str [source]
Return the name of the file containing the pickled Tiled object.
- Parameters:
filename (Image) -- Full image filename.
- Returns:
filename (str) -- Pickled object filename.
- get_data_filename(image_filename: str) str [source]
Return the name of the file containing the memory-mapped image data.
- Parameters:
filename (Image) -- Full image filename.
- Returns:
filename (str) -- Filename of the memory-mapped image data.