def from_json(cls,
session_data: dict,
monitor_delay: Optional[float] = None) \
-> "BehaviorSession":
"""
Parameters
----------
session_data
Dict of input data necessary to construct a session
monitor_delay
Monitor delay. If not provided, will use an estimate.
To provide this value, see for example
allensdk.brain_observatory.behavior.data_objects.stimuli.util.
calculate_monitor_delay
Returns
-------
`BehaviorSession` instance
"""
behavior_session_id = BehaviorSessionId.from_json(
dict_repr=session_data)
stimulus_file = StimulusFile.from_json(dict_repr=session_data)
stimulus_timestamps = StimulusTimestamps.from_json(
dict_repr=session_data)
running_acquisition = RunningAcquisition.from_json(
dict_repr=session_data)
raw_running_speed = RunningSpeed.from_json(dict_repr=session_data,
filtered=False)
running_speed = RunningSpeed.from_json(dict_repr=session_data)
metadata = BehaviorMetadata.from_json(dict_repr=session_data)
if monitor_delay is None:
monitor_delay = cls._get_monitor_delay()
licks, rewards, stimuli, task_parameters, trials = \
cls._read_data_from_stimulus_file(
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
trial_monitor_delay=monitor_delay
)
date_of_acquisition = DateOfAcquisition.from_json(
dict_repr=session_data)\
.validate(
stimulus_file=stimulus_file,
behavior_session_id=behavior_session_id.value)
return BehaviorSession(behavior_session_id=behavior_session_id,
stimulus_timestamps=stimulus_timestamps,
running_acquisition=running_acquisition,
raw_running_speed=raw_running_speed,
running_speed=running_speed,
metadata=metadata,
licks=licks,
rewards=rewards,
stimuli=stimuli,
task_parameters=task_parameters,
trials=trials,
date_of_acquisition=date_of_acquisition)
def from_json(cls,
dict_repr: dict,
filtered: bool = True,
zscore_threshold: float = 10.0) -> "RunningSpeed":
stimulus_file = StimulusFile.from_json(dict_repr)
stimulus_timestamps = StimulusTimestamps.from_json(dict_repr)
running_speed = cls._get_running_speed_df(stimulus_file,
stimulus_timestamps,
filtered, zscore_threshold)
return cls(running_speed=running_speed,
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
filtered=filtered)
def from_json(
cls,
dict_repr: dict,
) -> "RunningAcquisition":
stimulus_file = StimulusFile.from_json(dict_repr)
stimulus_timestamps = StimulusTimestamps.from_json(dict_repr)
running_acq_df = get_running_df(
data=stimulus_file.data,
time=stimulus_timestamps.value,
)
running_acq_df.drop("speed", axis=1, inplace=True)
return cls(
running_acquisition=running_acq_df,
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
)