Tiled

Image tiling module

class PixelValueModel(*, values: list[float | None])[source]

Bases: BaseModel

Pydantic model class for VisiOmatic pixel values.

Parameters:

values (list[float | None]) -- 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:
  • x (int) -- x coordinate of the pixel.

  • y (int) -- y coordinate of the pixel.

  • values (list[float, None]) -- 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), '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:
  • filename (str | Path,) -- Path to the image.

  • 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_mosaic(images: List[Image]) None[source]

Stitch together several images to make a mosaic

Parameters:

images (list[Image]) -- list of input images.

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.

make_tiles() None[source]

Generate all tiles from the image.

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).

Parameters:
  • raster (ndarray) -- Input tile.

  • channel (int, optional) -- Data channel (first channel is 1)

  • colormap (str, optional) -- Colormap: 'grey' (default), 'jet', 'cold', or 'hot'.

  • quality (int, optional) -- JPEG quality (0-100)

Returns:

tile (bytes) -- JPEG bytestream of the tile.

get_encoded_tile = <methodtools._LruCacheWire object>[source]
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.

Parameters:
  • channels (tuple[int]) -- Tuple of data channels (first channel is 1).

  • pos (tuple[int, int]) -- Pixel coordinates.

Returns:

value (~numpy.ndarray) -- Pixel value at the given position, or NaN outside of the frame boundaries.

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:
  • channels (tuple[int, ...] or None) -- Tuple of data channels (first channel is 1) or None for all channels.

  • pos1 (tuple[int, int]) -- Start pixel coordinates.

  • pos2 (tuple[int, int]) -- End pixel coordinates.

Returns:

profile (ProfileModel) -- Profile pydantic model of pixel value(s) along the line.

get_data()[source]

Get current memory-mapped image data.

Returns:

data (numpy.ndarray) -- Image data.

get_tiles()[source]

Get current memory-mapped tile data.

Returns:

data (numpy.ndarray) -- Tile data.

pickledTiled(filename: str, **kwargs) Tiled[source]

Return pickled Tiled object if available, or initialized otherwise.

Parameters:
  • filename (str | Path) -- Path to the image.

  • **kwargs (dict) -- Additional keyword arguments.

Returns:

tiled (object) -- Tiled object pickled from file 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.

get_tiles_filename(image_filename: str) str[source]

Return the name of the file containing the memory-mapped tile datacube.

Parameters:

filename (Image) -- Full image filename.

Returns:

filename (str) -- Filename of the memory mapped tile datacube.

get_image_filename(prefix: str) str[source]

Return the name of the file containing the memory-mapped image datacube.

Parameters:

prefix (str) -- Image name prefix.

Returns:

filename (str) -- Filename of the memory mapped tile datacube.