Interface Methods

Here you will find some information on the data and classes that are available after loading a CARTO3 study. cartoreader_lite.CartoStudy is the high level entry point to read CARTO3 studies.

class cartoreader_lite.CartoStudy(arg1, arg2=None, ablation_sites_kwargs=None, carto_map_kwargs=None)

High level class to easily read Carto3 archives, directories or buffered studies.

Parameters

• arg1 (str) –

  A path to either

  • A directory containing the study

  • A zip file with the study inside

  • A path to a previously saved study

  • A cartoreader_lite.low_level.study.CartoLLStudy instance

• arg2 (str, optional) – The name of the study to load, contained inside the directory or zip file. Has to be None when loading a pickled study. Will default to either the zip name, bottom most directory name or None, depending on your choice of arg1.

• ablation_sites_kwargs (Dict) – Optional keyword arguments to be passed to AblationSites

• carto_map_kwargs (Dict) – Optional keyword arguments to be passed to CartoMap

name: str

The name of the study

ablation_data: cartoreader_lite.high_level.study.AblationSites

Detailed information about the ablation sites and their readings over time

maps: List[cartoreader_lite.high_level.study.CartoMap]

All recorded maps associated with this study

aux_meshes: List[cartoreader_lite.low_level.study.CartoAuxMesh]

Auxiliary meshes generated by the CARTO system, not associated with any specific map, e.g. CT segmentations from CARTOSeg.

aux_mesh_reg_mat: numpy.ndarray

4x4 affine registration matrix to map the auxiliary meshes.

save(file: Optional[Union[IO, os.PathLike]] = None)

Backup the current study into a pickled and compressed file or buffer.

Parameters

file (Union[IO, PathLike], optional) –

Target to write the study to. Can be on of the following:

• Name of the file which the study will be written to

• A file, or buffer handle to write to

Will default to the study name with the ending .pkl.gz

static load_pickled_study(file: Union[IO, os.PathLike]) → cartoreader_lite.high_level.study.CartoStudy

Will load a pickled study either from

Parameters

file (Union[IO, PathLike]) – [description]

Returns

[description]

Return type

CartoStudy

Internal High Level Classes

These classes do not need to be manually created, but should be automatically created when generating a CartoStudy. Their most important members are described below.

class cartoreader_lite.CartoMap(ll_map: cartoreader_lite.low_level.study.CartoLLMap, *simplify_args, **simplify_kwargs)

High level container for carto maps with the associated point data and mesh.

Parameters

ll_map (CartoLLMap) – The low level study to load and simplify

points: pandas.core.frame.DataFrame

Recorded point data associated with this map. The column detail returns the associated CartoPointDetailData where the ECGs and EGMs can be found.

mesh: pyvista.core.pointset.UnstructuredGrid

Mesh associated with the map

class cartoreader_lite.CartoPointDetailData(main_data: pandas.core.series.Series, raw_data: Tuple[Dict[str, Dict], Dict[str, Dict]], remove_egm_header_numbers=True)

Detailed data associated to a CARTO3 point.

Parameters

• main_data (pd.Series) – Metadata of the point, such as ID and mean position

• raw_data (Tuple[Dict[str, Dict], Dict[str, Dict]]) – Raw data provided by cartoreader_lite.low_level.study.CartoLLMap, including details such as EGMs.

• remove_egm_header_numbers (bool, optional) – If true, the EGM header numbers will be discarded (e.g. I(110) -> I). By default True

id: int

Carto generated ID of the point

pos: numpy.ndarray

Position of the point in 3D

cath_orientation: numpy.ndarray

3D-Orientation of the catheter while recording the point

cath_id: int

ID of the Catheter

woi: numpy.ndarray

WOI. Only recordings located in this window are deemed valid.

start_time: int

System start time of the recording

ref_annotation: int

Reference annotation to synchronize all recordings

map_annotation: int

Annotation of the activation of this point, also often called LAT

uni_volt: float

Unipolar voltage magnitdue

bip_volt: float

Bipolar voltage magnitdue

connectors: List[str]

List of the recorded connector names

ecg_gain: float

Gain of the recorded ECGs

ecg_metadata: object

Additional provided metadata regarding the ECGs or EGMs

surface_ecg: pandas.core.frame.DataFrame

Recorded surface ECG. Has type np.int16 and needs to be multiplied by ecg_gain to get the ECG in Volts

egm: pandas.core.frame.DataFrame

Recorded electrograms at the point through the connectors. Naming and columns differ for each setup

class cartoreader_lite.AblationSites(visitag_data: Dict[str, pandas.core.frame.DataFrame], resample_unified_time=True, position_to_vec=True)

High level class for easily referencing ablation sites read from the visitag files. Automatically generated from CartoStudy.

Parameters

• visitag_data (Dict[str, pd.DataFrame]) – Visitag data read by the low level function .low_level.visitags.read_visitag_dir.

• resample_unified_time (bool, optional) – If true, the visitag file with different time intervals and frequencies will be resampled into a single pandas Dataframe with unified time steps. By default True

• position_to_vec (bool, optional) – If true, the entries X, Y, Z of the dataframes will be unified in the resulting tables to a single 3D vector pos. By default, True

session_rf_data: List[Tuple[int, pandas.core.frame.DataFrame]] = None

Ablation data (impedance, power, temperature, …) provided by the low level classes. Only present if resample_unified_time was False

session_force_data: List[Tuple[int, pandas.core.frame.DataFrame]] = None

Force data provided by the low level classes. Only present if resample_unified_time was False

session_avg_data: pandas.core.frame.DataFrame

Contains average data of each ablation session, such as RFIndex, average force and position

session_time_data: List[Tuple[int, pandas.core.frame.DataFrame]]

List of time data associated with each Ablation session. Each item contains the session ID + the ablation data over the course of the session

class cartoreader_lite.CartoAuxMesh(xml_h: xml.etree.ElementTree.Element, path_prefix: str, load=True)

Class that holds auxiliary meshes of the CARTO system, e.g. generated by CartoSeg.

Parameters

• xml_h (Element) – XML entry point from where to read the auxiliary meshes

• path_prefix (str) – Path prefix pointing to the directory to read from

• load (bool, optional) – If true, the meshes will be read and buffered immediately. If false, only the names will be loaded and load_mesh() will need to be called later. By default True

mesh_data: pyvista.core.pointset.UnstructuredGrid

The loaded mesh

metadata: Dict[str, str]

Metadata associated with the mesh, given by the XML tags

affine: numpy.ndarray

4x4 affine transformation matrix given by CARTO

mesh_path: str

Full path to the mesh file

name: str

Name of the mesh

load_mesh()

Loads the mesh with the given name into the memory

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