es_sfgtools.data_models.metadata package

class es_sfgtools.data_models.metadata.Benchmark(*, name: str, benchmarkID: str | None = '', aPrioriLocation: Location | None = None, start: Annotated[datetime | None, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))] = None, end: Annotated[datetime | None, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))] = None, dropPointLocation: Location | None = None, transponders: List[Transponder] | None = None)

Bases: AttributeUpdater, BaseModel

aPrioriLocation: Location | None
benchmarkID: str | None
dropPointLocation: Location | None
end: datetime | None
get_transponder_by_datetime(dt: datetime) → Transponder | None

Get the transponder for a given datetime.

Parameters:

dt (datetime) – The datetime to get the transponder for.

Returns:

The transponder, or None if no transponder is found for the given datetime.

Return type:

Optional[Transponder]

model_computed_fields = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config = {}

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

model_fields = {'aPrioriLocation': FieldInfo(annotation=Union[Location, NoneType], required=False, default=None, description='The a priori location of the benchmark'), 'benchmarkID': FieldInfo(annotation=Union[str, NoneType], required=False, default='', description='The benchmark ID'), 'dropPointLocation': FieldInfo(annotation=Union[Location, NoneType], required=False, default=None, description='The drop point location of the benchmark'), 'end': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, description='The end date of the benchmark', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'name': FieldInfo(annotation=str, required=True, description='The name of the benchmark'), 'start': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, description='The start date of the benchmark', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'transponders': FieldInfo(annotation=Union[List[Transponder], NoneType], required=False, default_factory=list, description='The transponders attached to the benchmark')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str
start: datetime | None
transponders: List[Transponder] | None
class es_sfgtools.data_models.metadata.Campaign(*, name: str, type: str, vesselCode: str, start: Annotated[datetime, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))], end: Annotated[datetime, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))], vessel: Vessel | None = None, principalInvestigator: str | None = None, launchVesselName: str | None = None, recoveryVesselName: str | None = None, cruiseName: str | None = None, technicianName: str | None = None, technicianContact: str | None = None, surveys: List[Survey] = None)

Bases: AttributeUpdater, BaseModel

Represents a campaign, which is a collection of surveys.

class Config

Bases: object

arbitrary_types_allowed = True
check_survey_times()

Checks that survey times within the campaign do not overlap.

Raises:

ValueError – If any survey times overlap.

cruiseName: str | None
end: datetime
get_survey_by_datetime(dt: datetime) → Survey

Returns the survey that encompasses the given datetime.

Parameters:

dt (datetime) – The datetime to check against surveys.

Returns:

The Survey object that contains the given datetime.

Return type:

Survey

Raises:

ValueError – If no survey is found for the given datetime.

launchVesselName: str | None
model_computed_fields = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config = {'arbitrary_types_allowed': True}

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

model_fields = {'cruiseName': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'end': FieldInfo(annotation=datetime, required=True, description='The end date & time of the campaign', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'launchVesselName': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True, description='The name of the campaign in the format YYYY_A_VVVV'), 'principalInvestigator': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'recoveryVesselName': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'start': FieldInfo(annotation=datetime, required=True, description='The start date & time of the campaign', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'surveys': FieldInfo(annotation=List[Survey], required=False, default_factory=list), 'technicianContact': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'technicianName': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'type': FieldInfo(annotation=str, required=True, description='The type of the campaign (deploy | measure | etc)'), 'vessel': FieldInfo(annotation=Union[Vessel, NoneType], required=False, default=None, description='Instatiate Vessel object'), 'vesselCode': FieldInfo(annotation=str, required=True, description='The 4 digit vessel code, associated with a vessel metadata file')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str
principalInvestigator: str | None
recoveryVesselName: str | None
start: datetime
surveys: List[Survey]
technicianContact: str | None
technicianName: str | None
type: str
vessel: Vessel | None
vesselCode: str
class es_sfgtools.data_models.metadata.Site(*, names: List[str], networks: List[str], timeOrigin: Annotated[datetime, Ge(ge=datetime.datetime(1901, 1, 1, 0, 0))], localGeoidHeight: float | None = 0, arrayCenter: Location | None = None, campaigns: List[Campaign] = None, benchmarks: List[Benchmark] = None, referenceFrames: List[ReferenceFrame] = None)

Bases: BaseModel

class Config

Bases: object

json_encoders = {<class 'datetime.datetime'>: <function Site.Config.<lambda>>}
validate_assignment = True
arrayCenter: Location | None
benchmarks: List[Benchmark]
campaigns: List[Campaign]
export_site(filepath: str)
classmethod from_json(filepath: str) → Site
localGeoidHeight: float | None
model_computed_fields = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config = {'json_encoders': {<class 'datetime.datetime'>: <function Site.Config.<lambda>>}, 'validate_assignment': True}

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

model_fields = {'arrayCenter': FieldInfo(annotation=Union[Location, NoneType], required=False, default=None, description='The array center of the site'), 'benchmarks': FieldInfo(annotation=List[Benchmark], required=False, default_factory=list, description='The benchmarks associated with the site'), 'campaigns': FieldInfo(annotation=List[Campaign], required=False, default_factory=list, description='The campaigns associated with the site'), 'localGeoidHeight': FieldInfo(annotation=Union[float, NoneType], required=False, default=0, description='The local geoid height of the site'), 'names': FieldInfo(annotation=List[str], required=True, description='The names of the site, including the 4 character ID'), 'networks': FieldInfo(annotation=List[str], required=True, description='A list networks the site is part of'), 'referenceFrames': FieldInfo(annotation=List[ReferenceFrame], required=False, default_factory=list, description='The reference frames used for the site'), 'timeOrigin': FieldInfo(annotation=datetime, required=True, description='The time origin of the site', metadata=[Ge(ge=datetime.datetime(1901, 1, 1, 0, 0))])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

names: List[str]
networks: List[str]
print_json()
referenceFrames: List[ReferenceFrame]
return_tats_for_campaign(campaign_name: str) → List[Dict[str, Any]] | None

Return all TATs for a given campaign

Parameters:

campaign_name (str) – The name of the campaign

Returns:

A list of dictionaries containing Benchmark name, Transponder address, and TAT

Return type:

List[Dict[str, Any]]

run_component(component_type: TopLevelSiteGroups, component_metadata: dict, add_new: bool = False, update: bool = False, delete: bool = False)

Generic add, update or delete equipment for the site

run_sub_component(component_type: TopLevelSiteGroups, component_name: str, sub_component_type: SubLevelSiteGroups, sub_component_metadata: dict, add_new: bool = False, update: bool = False, delete: bool = False)

Generic add, update or delete sub-components (e.g Transponder attached to Benchmark, Survey to campaign) for the site.

timeOrigin: datetime
top_level_map_components: ClassVar[Dict[str, Any]] = {TopLevelSiteGroups.BENCHMARKS: (<function Site.<lambda>>, <class 'es_sfgtools.data_models.metadata.benchmark.Benchmark'>), TopLevelSiteGroups.CAMPAIGNS: (<function Site.<lambda>>, <class 'es_sfgtools.data_models.metadata.campaign.Campaign'>), TopLevelSiteGroups.REFERENCE_FRAMES: (<function Site.<lambda>>, <class 'es_sfgtools.data_models.metadata.site.ReferenceFrame'>)}
validate_components()

If there are no benchmarks, transponders, campaigns, or surveys, print a warning.

class es_sfgtools.data_models.metadata.Survey(*, id: str, type: str | SurveyType, benchmarkIDs: List[str], start: Annotated[datetime, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))], end: Annotated[datetime, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))], notes: str | None = None, commands: str | None = None)

Bases: AttributeUpdater, BaseModel

Represents a single survey within a campaign.

benchmarkIDs: List[str]
commands: str | None
end: datetime
id: str
model_computed_fields = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config = {}

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

model_fields = {'benchmarkIDs': FieldInfo(annotation=List[str], required=True, description='Benchmark IDs associated with the survey'), 'commands': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Log of commands'), 'end': FieldInfo(annotation=datetime, required=True, description='The end date & time of the survey', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'id': FieldInfo(annotation=str, required=True, description='The unique ID of the survey'), 'notes': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Any additional notes about the survey'), 'start': FieldInfo(annotation=datetime, required=True, description='The start date & time of the survey', metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'type': FieldInfo(annotation=Union[str, SurveyType], required=True, description='The type of the survey (e.g. circle | fixed point | mixed)')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_post_init(context: Any, /) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self – The BaseModel instance.

  • context – The context.

notes: str | None
start: datetime
type: str | SurveyType
class es_sfgtools.data_models.metadata.SurveyType(value)

Bases: str, Enum

CENTER = 'center'
CIRCLE = 'circledrive'
FIXED_POINT = 'fixedpoint'
GARPOS = 'garpos'
MIXED = 'mixed'
MOVEAROUND = 'movearound'
OTHER = 'other'
class es_sfgtools.data_models.metadata.Vessel(*, name: str, type: str, model: str, serialNumber: str | None = None, start: Annotated[datetime | None, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))] = None, end: Annotated[datetime | None, Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))] = None, imuSensors: List[ImuSensor] = None, atdOffsets: List[AtdOffset] = None, gnssAntennas: List[GnssAntenna] = None, gnssReceivers: List[GnssReceiver] = None, acousticTransducers: List[AcousticTransducer] = None, acousticTransceivers: List[AcousticTransceiver] = None)

Bases: AttributeUpdater, BaseModel

acousticTransceivers: List[AcousticTransceiver]
acousticTransducers: List[AcousticTransducer]
atdOffsets: List[AtdOffset]
classmethod check_required_fields(value)
end: datetime | None
equipment_map: ClassVar[Dict[str, Any]] = {EquipmentType.ACOUSTIC_TRANSCEIVER: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.AcousticTransceiver'>), EquipmentType.ACOUSTIC_TRANSDUCER: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.AcousticTransducer'>), EquipmentType.ATD_OFFSETS: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.AtdOffset'>), EquipmentType.GNSS_ANTENNAS: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.GnssAntenna'>), EquipmentType.GNSS_RECEIVERS: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.GnssReceiver'>), EquipmentType.IMU_SENSORS: (<function Vessel.<lambda>>, <class 'es_sfgtools.data_models.metadata.vessel.ImuSensor'>)}
export_vessel(filepath: str)

Export vessel data to a JSON file.

Parameters:

filepath (str) – The path to the JSON file.

classmethod from_json(filepath: str) → Vessel

Import vessel data from a JSON file.

Parameters:

filepath (str) – The path to the JSON file.

Returns:

The vessel object.

Return type:

Vessel

gnssAntennas: List[GnssAntenna]
gnssReceivers: List[GnssReceiver]
imuSensors: List[ImuSensor]
model: str
model_computed_fields = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config = {}

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

model_fields = {'acousticTransceivers': FieldInfo(annotation=List[AcousticTransceiver], required=False, default_factory=list), 'acousticTransducers': FieldInfo(annotation=List[AcousticTransducer], required=False, default_factory=list), 'atdOffsets': FieldInfo(annotation=List[AtdOffset], required=False, default_factory=list), 'end': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'gnssAntennas': FieldInfo(annotation=List[GnssAntenna], required=False, default_factory=list), 'gnssReceivers': FieldInfo(annotation=List[GnssReceiver], required=False, default_factory=list), 'imuSensors': FieldInfo(annotation=List[ImuSensor], required=False, default_factory=list), 'model': FieldInfo(annotation=str, required=True, description='The model of the vessel'), 'name': FieldInfo(annotation=str, required=True, description='The 4 digit name of the vessel'), 'serialNumber': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'start': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None, metadata=[Gt(gt=datetime.datetime(1901, 1, 1, 0, 0))]), 'type': FieldInfo(annotation=str, required=True, description='The type of the vessel. e.g. waveglider')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

name: str
print_json()

Print the vessel data as a JSON string.

run_equipment(serial_number: str, equipment_type: EquipmentType, equipment_metadata: dict, add_new: bool = False, update: bool = False, delete: bool = False)

Add, update, or delete a survey vessel equipment.

Parameters:

  • serial_number (str) – The serial number of the equipment.

  • equipment_type (EquipmentType) – The type of the equipment.

  • equipment_metadata (dict) – The metadata of the equipment.

  • add_new (bool, optional) – Whether to add a new equipment, by default False.

  • update (bool, optional) – Whether to update an existing equipment, by default False.

  • delete (bool, optional) – Whether to delete an existing equipment, by default False.

serialNumber: str | None
start: datetime | None
type: str
es_sfgtools.data_models.metadata.classify_survey_type(survey_type: str) → SurveyType

Classifies the survey type based on the provided string.

Parameters:

survey_type (str) – The survey type as a string.

Returns:

The classified SurveyType.

Return type:

SurveyType

Raises:

ValueError – If the survey type is not recognized.

es_sfgtools.data_models.metadata.import_site(filepath: str)

Import site data from a JSON file.

es_sfgtools.data_models.metadata.import_vessel(filepath: str) → Vessel

Import vessel data from a JSON file.

Parameters:

filepath (str) – The path to the JSON file.

Returns:

The vessel object.

Return type:

Vessel

Submodules