floodlight.io.kinexon

Creates a dictionary from the pID_dict linking the identifier to the xID.

Parameters:

  • pID_dict (Dict[str, Dict[str, List[str]]]) – Nested dictionary that stores information about the pIDs from every player- identifying column in every group. The format is pID_dict[group][identifying_column] = [pID1, pID2, ..., pIDn]. When recording and exporting Kinexon data, the pID can be stored in different columns. Player-identifying columns are "sensor_id", "mapped_id", and "full_name". If the respective column is in the recorded data, its pIDs are listed in pID_dict.

  • identifier (str, optional) – Column-name of personal identifier in Kinexon.csv-file, defaults to None. Can be one of: "sensor_id", "mapped_id", "name".

    When recording and exporting Kinexon data, the pID can be stored in different columns. Player-identifying columns are "sensor_id", "mapped_id", and "full_name". If specified to one of the above, keys in links will be the pIDs in that column. If not specified, it will use one of the columns, favoring "name" over "mapped_id" over "sensor_id".

Returns:

links – Link-dictionary of the form links[group][identifier-ID] = xID.

Return type:

Dict[str, Dict[str, int]]

floodlight.io.kinexon.get_column_names_from_csv(filepath_data, delimiter=',')[source]

Reads first line of a Kinexon.csv-file and extracts the column names.

Parameters:

  • filepath_data (str or pathlib.Path) – Full path to Kinexon.csv-file.

  • delimiter (str) – Column delimiter used in the Kinexon.csv file. Defaults to ‘,’.

Returns:

columns – List with every column name of the .csv-file.

Return type:

List[str]

floodlight.io.kinexon.get_meta_data(filepath_data, delimiter=',')[source]

Reads Kinexon’s position data file and extracts meta-data about groups, sensors, length and framerate.

Parameters:

  • filepath_data (str or pathlib.Path) – Full path to Kinexon.csv-file.

  • delimiter (str, optional) – Column delimiter used in the Kinexon.csv file. Defaults to ‘,’.

Return type:

Tuple[Dict[str, Dict[str, List[str]]], int, int, int]

Returns:

  • pID_dict (Dict[str, Dict[str, List[str]]],) – Nested dictionary that stores information about the pIDs from every player- identifying column in every group. ‘pID_dict[group_identifier][identifying_column] = [pID1, pID2, …, pIDn]’ When recording and exporting Kinexon data, the pID can be stored in different columns. Player-identifying columns are “sensor_id”, “mapped_id”, and “full_name”. If the respective column is in the recorded data, its pIDs are listed in pID_dict. As with pID, group ids can be stored in different columns. Group-identifying columns are “group_name” and “group_id”. If both are available, group_name will be favored over group_id as the group_identifier.

  • number_of_frames (int) – Number of frames from the first to the last recorded frame.

  • framerate (int) – Estimated framerate in frames per second. Estimated from the smallest difference between two consecutive frames.

  • t_null (int) – Timestamp of the first recorded frame

floodlight.io.kinexon.read_position_data_csv(filepath_data, delimiter=',', teamsheets=None, as_dict=False)[source]

Parses a Kinexon .csv file and extracts position data.

Parameters:

  • filepath_data (str or pathlib.Path) – Full path to Kinexon .csv-file.

  • delimiter (str, optional) – Column delimiter used in the Kinexon.csv file. Defaults to ‘,’.

  • teamsheets (dict of Teamsheet, optional) – Pre-defined Teamsheet objects keyed by group ID. If None (default), teamsheets will be created automatically from metadata.

  • as_dict (bool, optional) – If True, returns teamsheets as a dictionary keyed by group. If False (default), returns teamsheets as a list sorted by group.

Returns:

xy_objects – If as_dict == False (default) returns a list of XY-objects for the whole game, one per group. The order of groups is ascending according to their group_id. If as_dict == True returns a dictionary with entries {group_id: XY} for each group in the data. If no groups are specified in the file, all data gets assigned to a dummy group “0”. The order inside the groups is ascending according to their appearance in the data.

Return type:

List[XY] or Dict[str, XY]

floodlight.io.kinexon.read_teamsheets_from_csv(filepath_data, delimiter=',', as_dict=False, player_id=None)[source]

Reads Kinexon .csv data and returns Teamsheet objects per group.

Parameters:

  • filepath_data (str or pathlib.Path) – Full path to Kinexon .csv file.

  • delimiter (str, optional) – Delimiter used in the file. Defaults to ‘,’.

  • as_dict (bool, optional) – If True, return teamsheets as dict keyed by group. If False (default), return a list sorted by group.

  • player_id (str, optional) – Column name to use as the primary player identifier in the Teamsheet’s “player” column. Must match one of the available identifiers in the Kinexon data (e.g., “name”, “mapped_id”, “sensor_id”, “number”). If None (default), the function will automatically choose the best available identifier, prioritizing: “name” > “mapped_id” > “sensor_id” > “number”.

Returns:

teamsheets – List or dictionary with teamsheets for each group.

Return type:

List[Teamsheet] or Dict[str, Teamsheet]