ibllib.io.extractors.ephys_passive

Functions

`extract_passive_periods`
`extract_replay_debug`
`extract_rfmapping`
`extract_task_replay`
`skip_task_replay`	Find whether the task replay portion of the passive stimulus has been shown

Classes

PassiveChoiceWorld

skip_task_replay(session_path: str, task_collection: str = 'raw_passive_data') → bool[source]

Find whether the task replay portion of the passive stimulus has been shown

Parameters:

session_path (str) – the path to a session
task_collection (str) – collection containing task data

Returns:

whether or not the task replay has been run

Return type:

bool

extract_passive_periods(session_path: str, sync_collection: str = 'raw_ephys_data', sync: dict = None, sync_map: dict = None, tmin=None, tmax=None) → DataFrame[source]

extract_rfmapping(session_path: str, sync_collection: str = 'raw_ephys_data', task_collection: str = 'raw_passive_data', sync: dict = None, sync_map: dict = None, trfm: array = None) → Tuple[array, array][source]

extract_task_replay(session_path: str, sync_collection: str = 'raw_ephys_data', task_collection: str = 'raw_passive_data', sync: dict = None, sync_map: dict = None, treplay: array = None) → Tuple[DataFrame, DataFrame][source]

extract_replay_debug(session_path: str, sync_collection: str = 'raw_ephys_data', task_collection: str = 'raw_passive_data', sync: dict = None, sync_map: dict = None, treplay: array = None, ax: axes = None) → Tuple[DataFrame, DataFrame][source]

class PassiveChoiceWorld(session_path=None)[source]

Bases: BaseExtractor

save_names = ('_ibl_passivePeriods.intervalsTable.csv', '_ibl_passiveRFM.times.npy', '_ibl_passiveGabor.table.csv', '_ibl_passiveStims.table.csv')

The filenames of each extracted dataset, or None if array should not be saved.

Type:: tuple of str

var_names = ('passivePeriods_df', 'passiveRFM_times', 'passiveGabor_df', 'passiveStims_df')

A list of names for the extracted variables. These become the returned output keys.

Type:: tuple of str