`observations_processing`¶

Functions¶

`observation_filter`(args, *kwargs)	Overloaded function.
`observation_set_splitter`(args, *kwargs)	Overloaded function.
`observation_parser`(args, *kwargs)	Overloaded function.

observation_filter(*args, **kwargs)¶

Overloaded function.

Overload 1: observation_filter(filter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterType, filter_value: typing.SupportsFloat | typing.SupportsIndex, filter_out: bool = True, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterBase

Create an observation filter with a single double value.

Parameters:

filter_type (tudatpy.estimation.observations.ObservationFilterType) – Type of observation filter.
filter_value (float) – Value to be used by the filter.
filter_out (bool, optional) – Whether to filter out observations that satisfy the condition (True) or keep them (False). Default is True.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation filter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationFilterBase

Overload 2: observation_filter(filter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterType, filter_value: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex], filter_out: bool = True, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterBase

Create an observation filter with a list of double values.

Parameters:

filter_type (tudatpy.estimation.observations.ObservationFilterType) – Type of observation filter.
filter_value (list[float]) – List of values to be used by the filter.
filter_out (bool, optional) – Whether to filter out observations that satisfy the condition (True) or keep them (False). Default is True.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation filter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationFilterBase

Overload 3: observation_filter(filter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterType, first_filter_value: typing.SupportsFloat | typing.SupportsIndex, second_filter_value: typing.SupportsFloat | typing.SupportsIndex, filter_out: bool = True, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterBase

Create an observation filter with two double values (e.g., for a time range).

Parameters:

filter_type (tudatpy.estimation.observations.ObservationFilterType) – Type of observation filter.
first_filter_value (float) – First value to be used by the filter (e.g., start time).
second_filter_value (float) – Second value to be used by the filter (e.g., end time).
filter_out (bool, optional) – Whether to filter out observations that satisfy the condition (True) or keep them (False). Default is True.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation filter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationFilterBase

Overload 4: observation_filter(filter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterType, filter_value: typing.Annotated[numpy.typing.ArrayLike, numpy.float64, "[m, 1]"], filter_out: bool = True, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterBase

Create an observation filter with a numpy array.

Parameters:

filter_type (tudatpy.estimation.observations.ObservationFilterType) – Type of observation filter.
filter_value (numpy.ndarray) – Numpy array to be used by the filter.
filter_out (bool, optional) – Whether to filter out observations that satisfy the condition (True) or keep them (False). Default is True.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation filter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationFilterBase

Overload 5: observation_filter(dependent_variable_settings: tudat::simulation_setup::ObservationDependentVariableSettings, filter_value: typing.Annotated[numpy.typing.ArrayLike, numpy.float64, "[m, 1]"], filter_out: bool = True, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationFilterBase

Create a dependent variable observation filter.

Parameters:

dependent_variable_settings (ObservationDependentVariableSettings) – Settings for the dependent variable to be used for filtering.
filter_value (numpy.ndarray) – Numpy array to be used by the filter.
filter_out (bool, optional) – Whether to filter out observations that satisfy the condition (True) or keep them (False). Default is True.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation filter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationFilterBase

observation_set_splitter(*args, **kwargs)¶

Overloaded function.

Overload 1: observation_set_splitter(splitter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterType, splitter_value: collections.abc.Sequence[typing.SupportsFloat | typing.SupportsIndex], min_number_observations: typing.SupportsInt | typing.SupportsIndex = 0) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterBase

Create an observation set splitter with a list of double values.

Parameters:

splitter_type (tudatpy.estimation.observations.ObservationSetSplitterType) – Type of observation set splitter.
splitter_value (list[float]) – List of values to be used by the splitter.
min_number_observations (int, optional) – Minimum number of observations per split set. Default is 0.

Returns:

An observation set splitter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationSetSplitterBase

Overload 2: observation_set_splitter(splitter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterType, splitter_value: typing.SupportsFloat | typing.SupportsIndex, min_number_observations: typing.SupportsInt | typing.SupportsIndex = 0) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterBase

Create an observation set splitter with a single double value.

Parameters:

splitter_type (tudatpy.estimation.observations.ObservationSetSplitterType) – Type of observation set splitter.
splitter_value (float) – Value to be used by the splitter.
min_number_observations (int, optional) – Minimum number of observations per split set. Default is 0.

Returns:

An observation set splitter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationSetSplitterBase

Overload 3: observation_set_splitter(splitter_type: tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterType, splitter_value: typing.SupportsInt | typing.SupportsIndex, min_number_observations: typing.SupportsInt | typing.SupportsIndex = 0) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationSetSplitterBase

Create an observation set splitter with a single integer value.

Parameters:

splitter_type (tudatpy.estimation.observations.ObservationSetSplitterType) – Type of observation set splitter.
splitter_value (int) – Value to be used by the splitter.
min_number_observations (int, optional) – Minimum number of observations per split set. Default is 0.

Returns:

An observation set splitter object.

Return type:

tudatpy.estimation.observations.observations_processing.ObservationSetSplitterBase

observation_parser(*args, **kwargs)¶

Overloaded function.

Overload 1: observation_parser() -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an empty observation parser.

Returns:: An empty observation parser object.
Return type:: tudatpy.estimation.observations.ObservationCollectionParser

Overload 2: observation_parser(observable_type: tudatpy.kernel.estimation.observable_models_setup.model_settings.ObservableType, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on observable type.

Parameters:

observable_type (ObservableType) – Observable type to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 3: observation_parser(observable_type_vector: collections.abc.Sequence[tudatpy.kernel.estimation.observable_models_setup.model_settings.ObservableType], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of observable types.

Parameters:

observable_type_vector (list[ObservableType]) – List of observable types to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 4: observation_parser(link_ends: collections.abc.Mapping[tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType, tudatpy.kernel.estimation.observable_models_setup.links.LinkEndId], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on link ends.

Parameters:

link_ends (dict[LinkEndType, LinkEndId]) – Link ends to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 5: observation_parser(link_ends_vector: collections.abc.Sequence[collections.abc.Mapping[tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType, tudatpy.kernel.estimation.observable_models_setup.links.LinkEndId]], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of link ends.

Parameters:

link_ends_vector (list[dict[LinkEndType, LinkEndId]]) – List of link ends to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 6: observation_parser(link_ends_str: str, is_reference_point: bool = False, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a link end string (body name).

Parameters:

link_ends_str (str) – Name of the body involved in the link end.
is_reference_point (bool, optional) – Whether the body is a reference point. Default is False.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 7: observation_parser(link_ends_str_vector: collections.abc.Sequence[str], is_reference_point: bool = False, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of link end strings (body names).

Parameters:

link_ends_str_vector (list[str]) – List of names of bodies involved in the link ends.
is_reference_point (bool, optional) – Whether the bodies are reference points. Default is False.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 8: observation_parser(link_end_id: tuple[str, str], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a link end ID.

Parameters:

link_end_id (tuple[str, str]) – Link end ID, as a tuple of (body name, station name).
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 9: observation_parser(link_end_ids_vector: collections.abc.Sequence[tuple[str, str]], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of link end IDs.

Parameters:

link_end_ids_vector (list[tuple[str, str]]) – List of link end IDs, each as a tuple of (body name, station name).
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 10: observation_parser(link_end_type: tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a link end type.

Parameters:

link_end_type (LinkEndType) – Link end type to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 11: observation_parser(link_end_types_vector: collections.abc.Sequence[tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of link end types.

Parameters:

link_end_types_vector (list[LinkEndType]) – List of link end types to parse.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 12: observation_parser(single_link_end: tuple[tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType, tudatpy.kernel.estimation.observable_models_setup.links.LinkEndId], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a single link end (type and ID).

Parameters:

single_link_end (tuple[LinkEndType, LinkEndId]) – A single link end, specified by its type and ID.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 13: observation_parser(single_link_ends_vector: collections.abc.Sequence[tuple[tudatpy.kernel.estimation.observable_models_setup.links.LinkEndType, tudatpy.kernel.estimation.observable_models_setup.links.LinkEndId]], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of single link ends (type and ID).

Parameters:

single_link_ends_vector (list[tuple[LinkEndType, LinkEndId]]) – A list of single link ends, each specified by its type and ID.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 14: observation_parser(time_bounds: tuple[typing.SupportsFloat | typing.SupportsIndex, typing.SupportsFloat | typing.SupportsIndex], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on time bounds.

Parameters:

time_bounds (tuple[float, float]) – Time bounds (start and end time) for parsing.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 15: observation_parser(time_bounds_vector: collections.abc.Sequence[tuple[typing.SupportsFloat | typing.SupportsIndex, typing.SupportsFloat | typing.SupportsIndex]], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of time bounds.

Parameters:

time_bounds_vector (list[tuple[float, float]]) – List of time bounds (start and end time) for parsing.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 16: observation_parser(ancillary_settings: tudat::observation_models::ObservationAncillarySimulationSettings, use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on ancillary settings.

Parameters:

ancillary_settings (ObservationAncillarySimulationSettings) – Ancillary settings for parsing.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 17: observation_parser(ancillary_settings_vector: collections.abc.Sequence[tudat::observation_models::ObservationAncillarySimulationSettings], use_opposite_condition: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create an observation parser based on a list of ancillary settings.

Parameters:

ancillary_settings_vector (list[ObservationAncillarySimulationSettings]) – List of ancillary settings for parsing.
use_opposite_condition (bool, optional) – Whether to use the opposite of the default condition. Default is False.

Returns:

An observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Overload 18: observation_parser(observation_parsers: collections.abc.Sequence[tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser], combine_conditions: bool = False) -> tudatpy.kernel.estimation.observations.observations_processing.ObservationCollectionParser

Create a multi-type observation parser from a list of other parsers.

Parameters:

observation_parsers (list[tudatpy.estimation.observations.ObservationCollectionParser]) – List of observation parsers to combine.
combine_conditions (bool, optional) – If True, conditions are combined with AND (intersection). If False, with OR (union). Default is False.

Returns:

A multi-type observation parser object.

Return type:

tudatpy.estimation.observations.ObservationCollectionParser

Enumerations¶

`ObservationFilterType`	Enum for types of observation filters.
`ObservationSetSplitterType`	Enum for types of observation set splitters.
`ObservationParserType`	Enum for types of observation parsers.

class ObservationFilterType¶

Bases: pybind11_object

Enum for types of observation filters.

This enum defines the available types of observation filters that can be used to reject observations from a collection.

Members:

residual_filtering

absolute_value_filtering

epochs_filtering

time_bounds_filtering

dependent_variable_filtering

ObservationFilterType.name -> str

class ObservationSetSplitterType¶

Bases: pybind11_object

Enum for types of observation set splitters.

This enum defines the available types of observation set splitters that can be used to divide a collection of observations into multiple sets.

Members:

time_tags_splitter

time_interval_splitter

time_span_splitter

nb_observations_splitter

ObservationSetSplitterType.name -> str

class ObservationParserType¶

Bases: pybind11_object

Enum for types of observation parsers.

This enum defines the available types of observation parsers that can be used to select observations from a collection based on various criteria.

Members:

empty_parser

observable_type_parser

link_ends_parser

link_end_str_parser

link_end_id_parser

link_end_type_parser

single_link_end_parser

time_bounds_parser

ancillary_settings_parser

multi_type_parser

ObservationParserType.name -> str

Classes¶

ObservationCollectionParser

Base class for observation collection parsers.

class ObservationCollectionParser¶

Bases: pybind11_object

Base class for observation collection parsers.

This is the base class from which all observation collection parsers are derived. It is not intended to be instantiated directly.

observations_processing¶

Functions¶

Enumerations¶

Classes¶

`observations_processing`¶