ObservationSummaryStats#

class lsst.images.ObservationSummaryStats(*, version: int = 0, psfSigma: float = nan, psfArea: float = nan, psfIxx: float = nan, psfIyy: float = nan, psfIxy: float = nan, ra: float = nan, dec: float = nan, pixelScale: float = nan, zenithDistance: float = nan, expTime: float = nan, zeroPoint: float = nan, skyBg: float = nan, skyNoise: float = nan, meanVar: float = nan, raCorners: tuple[float, float, float, float] = <factory>, decCorners: tuple[float, float, float, float] = <factory>, astromOffsetMean: float = nan, astromOffsetStd: float = nan, nPsfStar: int = 0, psfStarDeltaE1Median: float = nan, psfStarDeltaE2Median: float = nan, psfStarDeltaE1Scatter: float = nan, psfStarDeltaE2Scatter: float = nan, psfStarDeltaSizeMedian: float = nan, psfStarDeltaSizeScatter: float = nan, psfStarScaledDeltaSizeScatter: float = nan, psfTraceRadiusDelta: float = nan, psfApFluxDelta: float = nan, psfApCorrSigmaScaledDelta: float = nan, maxDistToNearestPsf: float = nan, starEMedian: float = nan, starUnNormalizedEMedian: float = nan, starComa1Median: float = nan, starComa2Median: float = nan, starTrefoil1Median: float = nan, starTrefoil2Median: float = nan, starKurtosisMedian: float = nan, starE41Median: float = nan, starE42Median: float = nan, effTime: float = nan, effTimePsfSigmaScale: float = nan, effTimeSkyBgScale: float = nan, effTimeZeroPointScale: float = nan, magLim: float = nan)#

Bases: BaseModel

Attributes Summary

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.

Methods Summary

`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_legacy`(exposure_summary_stats)	Return an `ObservationSummaryStats` from a legacy `lsst.afw.image.ExposureSummaryStats`.
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes Documentation

model_computed_fields = {}#

model_config: ClassVar[ConfigDict] = {'ser_json_inf_nan': 'constants'}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra#

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields = {'astromOffsetMean': FieldInfo(annotation=float, required=False, default=nan, description='Astrometry match offset mean.'), 'astromOffsetStd': FieldInfo(annotation=float, required=False, default=nan, description='Astrometry match offset stddev.'), 'dec': FieldInfo(annotation=float, required=False, default=nan, description='Bounding box center Declination (degrees).'), 'decCorners': FieldInfo(annotation=tuple[float, float, float, float], required=False, default_factory=_default_corners, description='Declination of bounding box corners (degrees).'), 'effTime': FieldInfo(annotation=float, required=False, default=nan, description='Effective exposure time calculated from psfSigma, skyBg, and zeroPoint (seconds).'), 'effTimePsfSigmaScale': FieldInfo(annotation=float, required=False, default=nan, description='PSF scaling of the effective exposure time.'), 'effTimeSkyBgScale': FieldInfo(annotation=float, required=False, default=nan, description='Sky background scaling of the effective exposure time.'), 'effTimeZeroPointScale': FieldInfo(annotation=float, required=False, default=nan, description='Zeropoint scaling of the effective exposure time.'), 'expTime': FieldInfo(annotation=float, required=False, default=nan, description='Exposure time of the exposure (seconds).'), 'magLim': FieldInfo(annotation=float, required=False, default=nan, description='Magnitude limit at fixed SNR (default SNR=5) calculated from psfSigma, skyBg, zeroPoint, and readNoise.'), 'maxDistToNearestPsf': FieldInfo(annotation=float, required=False, default=nan, description='Maximum distance of an unmasked pixel to its nearest model psf star (pixels).'), 'meanVar': FieldInfo(annotation=float, required=False, default=nan, description='Mean variance of the weight plane (ADU**2).'), 'nPsfStar': FieldInfo(annotation=int, required=False, default=0, description='Number of stars used for psf model.'), 'pixelScale': FieldInfo(annotation=float, required=False, default=nan, description='Measured detector pixel scale (arcsec/pixel).'), 'psfApCorrSigmaScaledDelta': FieldInfo(annotation=float, required=False, default=nan, description='Delta (max - min) of the psf flux aperture correction factors scaled (divided) by the psfSigma evaluated on a grid of unmasked pixels.'), 'psfApFluxDelta': FieldInfo(annotation=float, required=False, default=nan, description='Delta (max - min) of the model psf aperture flux (with aperture radius of max(2, 3*psfSigma)) values evaluated on a grid of unmasked pixels.'), 'psfArea': FieldInfo(annotation=float, required=False, default=nan, description='PSF effective area (pixels**2).'), 'psfIxx': FieldInfo(annotation=float, required=False, default=nan, description='PSF shape Ixx (pixels**2).'), 'psfIxy': FieldInfo(annotation=float, required=False, default=nan, description='PSF shape Ixy (pixels**2).'), 'psfIyy': FieldInfo(annotation=float, required=False, default=nan, description='PSF shape Iyy (pixels**2).'), 'psfSigma': FieldInfo(annotation=float, required=False, default=nan, description='PSF determinant radius (pixels).'), 'psfStarDeltaE1Median': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars median E1 residual (starE1 - psfE1).'), 'psfStarDeltaE1Scatter': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars MAD E1 scatter (starE1 - psfE1).'), 'psfStarDeltaE2Median': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars median E2 residual (starE2 - psfE2).'), 'psfStarDeltaE2Scatter': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars MAD E2 scatter (starE2 - psfE2).'), 'psfStarDeltaSizeMedian': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars median size residual (starSize - psfSize).'), 'psfStarDeltaSizeScatter': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars MAD size scatter (starSize - psfSize).'), 'psfStarScaledDeltaSizeScatter': FieldInfo(annotation=float, required=False, default=nan, description='Psf stars MAD size scatter scaled by psfSize**2.'), 'psfTraceRadiusDelta': FieldInfo(annotation=float, required=False, default=nan, description='Delta (max - min) of the model psf trace radius values evaluated on a grid of unmasked pixels (pixels).'), 'ra': FieldInfo(annotation=float, required=False, default=nan, description='Bounding box center Right Ascension (degrees).'), 'raCorners': FieldInfo(annotation=tuple[float, float, float, float], required=False, default_factory=_default_corners, description='Right Ascension of bounding box corners (degrees).'), 'skyBg': FieldInfo(annotation=float, required=False, default=nan, description='Average sky background (ADU).'), 'skyNoise': FieldInfo(annotation=float, required=False, default=nan, description='Average sky noise (ADU).'), 'starComa1Median': FieldInfo(annotation=float, required=False, default=nan, description='Coma-like higher-order moment combination: median M30 + M12 of the stars used in the PSF model.'), 'starComa2Median': FieldInfo(annotation=float, required=False, default=nan, description='Coma-like higher-order moment combination: median M21 + M03 of the stars used in the PSF model.'), 'starE41Median': FieldInfo(annotation=float, required=False, default=nan, description='Fourth-order ellipticity-like higher-order moment combination: median M40 - M04 of the stars used in the PSF model.'), 'starE42Median': FieldInfo(annotation=float, required=False, default=nan, description='Fourth-order ellipticity-like higher-order moment combination: median 2*(M31 + M13) of the stars used in the PSF model.'), 'starEMedian': FieldInfo(annotation=float, required=False, default=nan, description='Median ellipticity (sqrt(starE1**2.0 + starE2**2.0)) of the stars used in the PSF model.'), 'starKurtosisMedian': FieldInfo(annotation=float, required=False, default=nan, description='Kurtosis-like higher-order moment combination: median M40 + 2*M22 + M04 of the stars used in the PSF model.'), 'starTrefoil1Median': FieldInfo(annotation=float, required=False, default=nan, description='Trefoil-like higher-order moment combination: median M30 - 3*M12 of the stars used in the PSF model.'), 'starTrefoil2Median': FieldInfo(annotation=float, required=False, default=nan, description='Trefoil-like higher-order moment combination: median 3*M21 - M03 of the stars used in the PSF model.'), 'starUnNormalizedEMedian': FieldInfo(annotation=float, required=False, default=nan, description='Median un-normalized ellipticity (sqrt((starXX - starYY)**2.0 + (2.0*starXY)**2.0)) of the stars used in the PSF model.'), 'version': FieldInfo(annotation=int, required=False, default=0, description='Version of the model.'), 'zenithDistance': FieldInfo(annotation=float, required=False, default=nan, description='Bounding box center zenith distance (degrees).'), 'zeroPoint': FieldInfo(annotation=float, required=False, default=nan, description='Mean zeropoint in detector (mag).')}#

model_fields_set#

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.

Methods Documentation

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

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:: 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.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]#

classmethod from_legacy(exposure_summary_stats: Any) → Self#: Return an ObservationSummaryStats from a legacy lsst.afw.image.ExposureSummaryStats.

classmethod from_orm(obj: Any) → Self#

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str#

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

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

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

!!! note

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

Args:

_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,: this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self#

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Args:

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.

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

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

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:

indent: Indentation to use in the JSON output. If None is passed, the output will be compact. ensure_ascii: If True, the output is guaranteed to have all incoming non-ASCII characters escaped.

If False (the default), these characters will be output as-is.

include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

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

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A JSON string representation of the model.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → dict[str, Any]#

Generates a JSON schema for a model class.

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. union_format: The format to use when combining schemas from unions together. Can be one of:

'any_of': Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

keyword to combine schemas (the default). - 'primitive_type_array': Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

schema_generator: 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.

Args:

params: Tuple of types of the class. Given a generic class: Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(context: Any, /) → None#: 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: MappingNamespace | None = None) → bool | None#

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args:: 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.

Validate a pydantic model instance.

Args:

obj: The object to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Args:

json_data: The JSON data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

Validate the given object with string data against the Pydantic model.

Args:

obj: The object containing string data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

Returns:

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

classmethod parse_obj(obj: Any) → Self#

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str#

classmethod update_forward_refs(**localns: Any) → None#

classmethod validate(value: Any) → Self#