nafc

Classes:

Nafc([stage_block, stim, reward, …])

A Two-alternative forced choice task.

Nafc_Gap([noise_amplitude])

A Mild variation of Nafc that starts continuous white noise that plays continuously while the task is active.

Nafc_Gap_Laser(laser_probability, …)

Gap detection task with ability to control lasers via TTL logic for optogenetics

Net_Node(id, upstream, port, listens[, …])

Drop in networking object to be given to any sub-object behind some external-facing Station object.

Task(*args, **kwargs)

Generic Task metaclass

odict

alias of collections.OrderedDict

Functions:

copy(x)

Shallow copy operation on arbitrary Python objects.

find_recursive(key, dictionary)

Find all instances of a key in a dictionary, recursively.

init_manager(stim)

class Nafc(stage_block=None, stim=None, reward=50, req_reward=False, punish_stim=False, punish_dur=100, correction=False, correction_pct=50.0, bias_mode=False, bias_threshold=20, current_trial=0, stim_light=True, **kwargs)[source]

Bases: autopilot.tasks.task.Task

A Two-alternative forced choice task.

(can’t have number as first character of class.)

Stages

  • request - compute stimulus, set request trigger in center port.

  • discrim - respond to input, set reward/punishment triggers on target/distractor ports

  • reinforcement - deliver reward/punishment, end trial.

Variables
  • target ("L", "R") – Correct response

  • distractor ("L", "R") – Incorrect response

  • stim – Current stimulus

  • response ("L", "R") – Response to discriminand

  • correct (0, 1) – Current trial was correct/incorrect

  • correction_trial (bool) – If using correction trials, last trial was a correction trial

  • trial_counter (itertools.count) – Which trial are we on?

  • discrim_playing (bool) – Is the stimulus playing?

  • bailed (0, 1) – Subject answered before stimulus was finished playing.

  • current_stage (int) – As each stage is reached, update for asynchronous event reference

Parameters
  • stage_block (threading.Event) – Signal when task stages complete.

  • stim (dict) –

    Stimuli like:

    "sounds": {
        "L": [{"type": "Tone", ...}],
        "R": [{"type": "Tone", ...}]
    }
    
  • reward (float) – duration of solenoid open in ms

  • req_reward (bool) – Whether to give a water reward in the center port for requesting trials

  • punish_stim (bool) – Do a white noise punishment stimulus

  • punish_dur (float) – Duration of white noise in ms

  • correction (bool) – Should we do correction trials?

  • correction_pct (float) – (0-1), What proportion of trials should randomly be correction trials?

  • bias_mode (False, “thresholded_linear”) – False, or some bias correction type (see managers.Bias_Correction )

  • bias_threshold (float) – If using a bias correction mode, what threshold should bias be corrected for?

  • current_trial (int) – If starting at nonzero trial number, which?

  • stim_light (bool) – Should the LED be turned blue while the stimulus is playing?

  • **kwargs

Attributes:

HARDWARE

PARAMS

PLOT

STAGE_NAMES

Classes:

TrialData()

Methods:

discrim(*args, **kwargs)

Stage 1: respond to input, set reward/punishment triggers on target/distractor ports

flash_leds()

flash lights for punish_dir

punish()

Flash lights, play punishment sound if set

reinforcement(*args, **kwargs)

Stage 2 - deliver reward/punishment, end trial.

request(*args, **kwargs)

Stage 0: compute stimulus, set request trigger in center port.

respond(pin)

Set self.response

stim_end()

called by stimulus callback

stim_start()

mark discrim_playing = true

STAGE_NAMES = ['request', 'discrim', 'reinforcement']
PARAMS = {'bias_mode': {'tag': 'Bias Correction Mode', 'type': 'list', 'values': {'None': 0, 'Proportional': 1, 'Thresholded Proportional': 2}}, 'bias_threshold': {'depends': {'bias_mode': 2}, 'tag': 'Bias Correction Threshold (%)', 'type': 'int'}, 'correction': {'tag': 'Correction Trials', 'type': 'bool'}, 'correction_pct': {'depends': {'correction': True}, 'tag': '% Correction Trials', 'type': 'int'}, 'punish_dur': {'tag': 'Punishment Duration (ms)', 'type': 'int'}, 'punish_stim': {'tag': 'White Noise Punishment', 'type': 'bool'}, 'req_reward': {'tag': 'Request Rewards', 'type': 'bool'}, 'reward': {'tag': 'Reward Duration (ms)', 'type': 'int'}, 'stim': {'tag': 'Sounds', 'type': 'sounds'}}
PLOT = {'chance_bar': True, 'data': {'correct': 'rollmean', 'response': 'segment', 'target': 'point'}, 'roll_window': 50}
class TrialData

Bases: tables.description.IsDescription

Attributes:

columns

laser

laser_duration

columns = {'DC_timestamp': StringCol(itemsize=26, shape=(), dflt=b'', pos=None), 'RQ_timestamp': StringCol(itemsize=26, shape=(), dflt=b'', pos=None), 'bailed': Int32Col(shape=(), dflt=0, pos=None), 'correct': Int32Col(shape=(), dflt=0, pos=None), 'correction': Int32Col(shape=(), dflt=0, pos=None), 'response': StringCol(itemsize=1, shape=(), dflt=b'', pos=None), 'target': StringCol(itemsize=1, shape=(), dflt=b'', pos=None), 'trial_num': Int32Col(shape=(), dflt=0, pos=None)}
laser = Int32Col(shape=(), dflt=0, pos=None)
laser_duration = Float32Col(shape=(), dflt=0.0, pos=None)
HARDWARE = {'LEDS': {'C': <class 'autopilot.hardware.gpio.LED_RGB'>, 'L': <class 'autopilot.hardware.gpio.LED_RGB'>, 'R': <class 'autopilot.hardware.gpio.LED_RGB'>, 'TOP': <class 'autopilot.hardware.gpio.Digital_Out'>}, 'POKES': {'C': <class 'autopilot.hardware.gpio.Digital_In'>, 'L': <class 'autopilot.hardware.gpio.Digital_In'>, 'R': <class 'autopilot.hardware.gpio.Digital_In'>}, 'PORTS': {'C': <class 'autopilot.hardware.gpio.Solenoid'>, 'L': <class 'autopilot.hardware.gpio.Solenoid'>, 'R': <class 'autopilot.hardware.gpio.Solenoid'>}}
request(*args, **kwargs)[source]

Stage 0: compute stimulus, set request trigger in center port.

Returns

With fields:

{
'target': self.target,
'trial_num' : self.current_trial,
'correction': self.correction_trial,
'type': stimulus type,
**stim.PARAMS
}

Return type

data (dict)

discrim(*args, **kwargs)[source]

Stage 1: respond to input, set reward/punishment triggers on target/distractor ports

Returns

With fields::

{ ‘RQ_timestamp’: datetime.datetime.now().isoformat(), ‘trial_num’: self.current_trial, }

Return type

data (dict)

reinforcement(*args, **kwargs)[source]

Stage 2 - deliver reward/punishment, end trial.

Returns

With fields:

 {
'DC_timestamp': datetime.datetime.now().isoformat(),
'response': self.response,
'correct': self.correct,
'bailed': self.bailed,
'trial_num': self.current_trial,
'TRIAL_END': True
}

Return type

data (dict)

punish()[source]

Flash lights, play punishment sound if set

respond(pin)[source]

Set self.response

Parameters

pin – Pin to set response to

stim_start()[source]

mark discrim_playing = true

stim_end()[source]

called by stimulus callback

set outside lights blue

flash_leds()[source]

flash lights for punish_dir

class Nafc_Gap(noise_amplitude=0.01, **kwargs)[source]

Bases: autopilot.tasks.nafc.Nafc

A Mild variation of Nafc that starts continuous white noise that plays continuously while the task is active.

Parameters
  • noise_amplitude (float) – Multiplier used to scale amplitude of continuous noise

  • **kwargs – passed to Nafc

Attributes:

PARAMS

Methods:

end()

Stop the task, ending the continuous white noise.

PARAMS = {'bias_mode': {'tag': 'Bias Correction Mode', 'type': 'list', 'values': {'None': 0, 'Proportional': 1, 'Thresholded Proportional': 2}}, 'bias_threshold': {'depends': {'bias_mode': 2}, 'tag': 'Bias Correction Threshold (%)', 'type': 'int'}, 'correction': {'tag': 'Correction Trials', 'type': 'bool'}, 'correction_pct': {'depends': {'correction': True}, 'tag': '% Correction Trials', 'type': 'int'}, 'noise_amplitude': {'tag': 'Amplitude of continuous white noise', 'type': 'float'}, 'punish_dur': {'tag': 'Punishment Duration (ms)', 'type': 'int'}, 'req_reward': {'tag': 'Request Rewards', 'type': 'bool'}, 'reward': {'tag': 'Reward Duration (ms)', 'type': 'int'}, 'stim': {'tag': 'Sounds', 'type': 'sounds'}}
end()[source]

Stop the task, ending the continuous white noise.

class Nafc_Gap_Laser(laser_probability: float, laser_mode: str, laser_freq: float, laser_duty_cycle: float, laser_durations: Union[str, list], **kwargs)[source]

Bases: autopilot.tasks.nafc.Nafc_Gap

Gap detection task with ability to control lasers via TTL logic for optogenetics

Note

Subclasses like these will be made obsolete with the completion of stimulus managers

Parameters
  • laser_probability (float)

  • laser_mode

  • laser_freq

  • laser_duty_cycle

  • laser_durations

Attributes:

HARDWARE

PARAMS

Classes:

TrialData()

Methods:

request(*args, **kwargs)

Stage 0: compute stimulus, set request trigger in center port.

PARAMS = {'bias_mode': {'tag': 'Bias Correction Mode', 'type': 'list', 'values': {'None': 0, 'Proportional': 1, 'Thresholded Proportional': 2}}, 'bias_threshold': {'depends': {'bias_mode': 2}, 'tag': 'Bias Correction Threshold (%)', 'type': 'int'}, 'correction': {'tag': 'Correction Trials', 'type': 'bool'}, 'correction_pct': {'depends': {'correction': True}, 'tag': '% Correction Trials', 'type': 'int'}, 'laser_durations': {'tag': 'Laser durations (ms), list-like [10, 20]. if blank, use durations from stimuli', 'type': 'str'}, 'laser_duty_cycle': {'tag': 'Laser Duty Cycle (0-1)', 'type': 'float'}, 'laser_freq': {'tag': 'Laser Pulse Frequency (Hz)', 'type': 'float'}, 'laser_mode': {'tag': 'Laser Mode', 'type': 'list', 'values': {'Both': 2, 'L': 0, 'R': 1}}, 'laser_probability': {'tag': 'Probability (of trials whose targets match laser_mode) of laser being turned on (0-1)', 'type': 'float'}, 'noise_amplitude': {'tag': 'Amplitude of continuous white noise', 'type': 'float'}, 'punish_dur': {'tag': 'Punishment Duration (ms)', 'type': 'int'}, 'req_reward': {'tag': 'Request Rewards', 'type': 'bool'}, 'reward': {'tag': 'Reward Duration (ms)', 'type': 'int'}, 'stim': {'tag': 'Sounds', 'type': 'sounds'}}
HARDWARE = {'LASERS': {'LR': <class 'autopilot.hardware.gpio.Digital_Out'>}, 'LEDS': {'C': <class 'autopilot.hardware.gpio.LED_RGB'>, 'L': <class 'autopilot.hardware.gpio.LED_RGB'>, 'R': <class 'autopilot.hardware.gpio.LED_RGB'>, 'TOP': <class 'autopilot.hardware.gpio.Digital_Out'>}, 'POKES': {'C': <class 'autopilot.hardware.gpio.Digital_In'>, 'L': <class 'autopilot.hardware.gpio.Digital_In'>, 'R': <class 'autopilot.hardware.gpio.Digital_In'>}, 'PORTS': {'C': <class 'autopilot.hardware.gpio.Solenoid'>, 'L': <class 'autopilot.hardware.gpio.Solenoid'>, 'R': <class 'autopilot.hardware.gpio.Solenoid'>}}
class TrialData

Bases: tables.description.IsDescription

Attributes:

columns

laser

laser_duration

columns = {'DC_timestamp': StringCol(itemsize=26, shape=(), dflt=b'', pos=None), 'RQ_timestamp': StringCol(itemsize=26, shape=(), dflt=b'', pos=None), 'bailed': Int32Col(shape=(), dflt=0, pos=None), 'correct': Int32Col(shape=(), dflt=0, pos=None), 'correction': Int32Col(shape=(), dflt=0, pos=None), 'response': StringCol(itemsize=1, shape=(), dflt=b'', pos=None), 'target': StringCol(itemsize=1, shape=(), dflt=b'', pos=None), 'trial_num': Int32Col(shape=(), dflt=0, pos=None)}
laser = Int32Col(shape=(), dflt=0, pos=None)
laser_duration = Float32Col(shape=(), dflt=0.0, pos=None)
request(*args, **kwargs)[source]

Stage 0: compute stimulus, set request trigger in center port.

Returns

With fields:

{
'target': self.target,
'trial_num' : self.current_trial,
'correction': self.correction_trial,
'type': stimulus type,
**stim.PARAMS
}

Return type

data (dict)