8.11.1

.pre-commit-config.yaml

@@ -20,7 +20,6 @@ repos:
 -   repo: https://github.com/pre-commit/pygrep-hooks
     rev: v1.10.0
     hooks:
-    -   id: python-check-blanket-type-ignore
     -   id: python-use-type-annotations
     -   id: python-no-log-warn
     -   id: text-unicode-replacement-char

CHANGELOG.md

@@ -3,15 +3,19 @@ Changelog
 
 -------------------------------
 
+8.11.1
+------
+* add GUI options for AdvancedChoiceWorld
+
 8.11.0
 ------
 * add check for availability of internet
 * add proper CLI for data transfer scripts
-* add option for disabling Bpod status LED
+* add control for disabling Bpod status LED
 * skip initialization of existing Bpod singleton
 * remember settings for status LED and GUI position
-* move update-check to separate thread
-* detect dud (less than 42 trials) and offer deletion
+* move update-check to separate thread 
+* detect duds (less than 42 trials) and offer deletion
 * various small bugfixes
 
 8.10.2

iblrig/__init__.py

@@ -4,7 +4,7 @@
 # 3) Check CI and eventually wet lab test
 # 4) Pull request to iblrigv8
 # 5) git tag the release in accordance to the version number below (after merge!)
-__version__ = '8.10.3'
+__version__ = '8.11.1'
 
 # The following method call will try to get post-release information (i.e. the number of commits since the last tagged
 # release corresponding to the one above), plus information about the state of the local repository (dirty/broken)

iblrig/base_biased_choice_world_params.yaml

@@ -0,0 +1,5 @@
+'BLOCK_INIT_5050': true
+'BLOCK_LEN_FACTOR': 60
+'BLOCK_LEN_MAX': 100
+'BLOCK_LEN_MIN': 20
+'BLOCK_PROBABILITY_SET': [0.2, 0.8]

iblrig/base_choice_world.py

@@ -10,9 +10,11 @@
 from string import ascii_letters
 import subprocess
 import time
+from typing import Literal, Annotated
 
 import numpy as np
 import pandas as pd
+from pydantic import BaseModel, Field
 
 from pybpodapi.protocol import StateMachine
 from pybpodapi.com.messaging.trial import Trial
@@ -32,6 +34,49 @@
 NTRIALS_INIT = 2000
 NBLOCKS_INIT = 100
 
+Probability = Annotated[float, Field(ge=0.0, le=1.0)]
+
+
+class ChoiceWorldParams(BaseModel):
+    AUTOMATIC_CALIBRATION: bool = True
+    ADAPTIVE_REWARD: bool = False
+    BONSAI_EDITOR: bool = False
+    CALIBRATION_VALUE: float = 0.067
+    CONTRAST_SET: list[Probability] = Field([1.0, 0.25, 0.125, 0.0625, 0.0], min_items=1)
+    CONTRAST_SET_PROBABILITY_TYPE: Literal["uniform", "skew_zero"] = 'uniform'
+    GO_TONE_AMPLITUDE: float = 0.0272
+    GO_TONE_DURATION: float = 0.11
+    GO_TONE_IDX: int = Field(2, ge=0)
+    GO_TONE_FREQUENCY: float = Field(5000, gt=0)
+    FEEDBACK_CORRECT_DELAY_SECS: float = 1
+    FEEDBACK_ERROR_DELAY_SECS: float = 2
+    FEEDBACK_NOGO_DELAY_SECS: float = 2
+    INTERACTIVE_DELAY: float = 0.0
+    ITI_DELAY_SECS: float = 1
+    NTRIALS: int = Field(2000, gt=0)
+    POOP_COUNT: bool = True
+    PROBABILITY_LEFT: Probability = 0.5
+    QUIESCENCE_THRESHOLDS: list[float] = Field(default=[-2, 2], min_length=2, max_length=2)
+    QUIESCENT_PERIOD: float = 0.2
+    RECORD_AMBIENT_SENSOR_DATA: bool = True
+    RECORD_SOUND: bool = True
+    RESPONSE_WINDOW: float = 60
+    REWARD_AMOUNT_UL: float = 1.5
+    REWARD_TYPE: str = 'Water 10% Sucrose'
+    STIM_ANGLE: float = 0.0
+    STIM_FREQ: float = 0.1
+    STIM_GAIN: float = 4.0  # wheel to stimulus relationship (degrees visual angle per mm of wheel displacement)
+    STIM_POSITIONS: list[float] = [-35, 35]
+    STIM_SIGMA: float = 7.0
+    STIM_TRANSLATION_Z: Literal[7, 8] = 7  # 7 for ephys, 8 otherwise. -p:Stim.TranslationZ-{STIM_TRANSLATION_Z} bonsai parameter
+    SYNC_SQUARE_X: float = 1.33
+    SYNC_SQUARE_Y: float = -1.03
+    USE_AUTOMATIC_STOPPING_CRITERIONS: bool = True
+    VISUAL_STIMULUS: str = 'GaborIBLTask / Gabor2D.bonsai'  # null / passiveChoiceWorld_passive.bonsai
+    WHITE_NOISE_AMPLITUDE: float = 0.05
+    WHITE_NOISE_DURATION: float = 0.5
+    WHITE_NOISE_IDX: int = 3
+
 
 class ChoiceWorldSession(
     iblrig.base_tasks.BaseSession,
@@ -43,6 +88,7 @@ class ChoiceWorldSession(
     iblrig.base_tasks.SoundMixin,
     iblrig.base_tasks.ValveMixin,
 ):
+    # task_params = ChoiceWorldParams()
     base_parameters_file = Path(__file__).parent.joinpath('base_choice_world_params.yaml')
 
     def __init__(self, *args, delay_secs=0, **kwargs):
@@ -639,6 +685,7 @@ class BiasedChoiceWorldSession(ActiveChoiceWorldSession):
     Biased choice world session is the instantiation of ActiveChoiceWorld where the notion of biased
     blocks is introduced.
     """
+    base_parameters_file = Path(__file__).parent.joinpath('base_biased_choice_world_params.yaml')
     protocol_name = "_iblrig_tasks_biasedChoiceWorld"
 
     def __init__(self, **kwargs):
@@ -713,9 +760,9 @@ class TrainingChoiceWorldSession(ActiveChoiceWorldSession):
     """
     protocol_name = "_iblrig_tasks_trainingChoiceWorld"
 
-    def __init__(self, training_phase=-1, adaptive_reward=-1.0, **kwargs):
+    def __init__(self, training_phase=-1, adaptive_reward=-1.0, adaptive_gain=None, **kwargs):
         super(TrainingChoiceWorldSession, self).__init__(**kwargs)
-        inferred_training_phase, inferred_adaptive_reward = self.get_subject_training_info()
+        inferred_training_phase, inferred_adaptive_reward, inferred_adaptive_gain = self.get_subject_training_info()
         if training_phase == -1:
             self.logger.critical(f"Got training phase: {inferred_training_phase}")
             self.training_phase = inferred_training_phase
@@ -728,6 +775,12 @@ def __init__(self, training_phase=-1, adaptive_reward=-1.0, **kwargs):
         else:
             self.logger.critical(f"Adaptive reward manually set to {adaptive_reward} uL")
             self.session_info["ADAPTIVE_REWARD_AMOUNT_UL"] = adaptive_reward
+        if adaptive_gain is None:
+            self.logger.critical(f"Got Adaptive gain {inferred_adaptive_gain} degrees/mm")
+            self.session_info["ADAPTIVE_GAIN_VALUE"] = inferred_adaptive_gain
+        else:
+            self.logger.critical(f"Adaptive gain manually set to {adaptive_gain} degrees/mm")
+            self.session_info["ADAPTIVE_GAIN_VALUE"] = adaptive_gain
         self.var = {
             "training_phase_trial_counts": np.zeros(6),
             "last_10_responses_sides": np.zeros(10),
@@ -742,25 +795,29 @@ def reward_amount(self):
     def get_subject_training_info(self):
         """
         Get the previous session's according to this session parameters and deduce the
-        training level and adaptive reward amount.
+        training level, adaptive reward amount and adaptive gain value
         :return:
         """
         try:
-            training_phase, adaptive_reward, _ = choiceworld.get_subject_training_info(
+            tinfo, _ = choiceworld.get_subject_training_info(
                 subject_name=self.session_info.SUBJECT_NAME,
                 default_reward=self.task_params.REWARD_AMOUNT_UL,
+                stim_gain=self.task_params.STIM_GAIN,
                 local_path=self.iblrig_settings['iblrig_local_data_path'],
                 remote_path=self.iblrig_settings['iblrig_remote_data_path'],
                 lab=self.iblrig_settings['ALYX_LAB'],
                 task_name=self.protocol_name,
             )
         except Exception:
             self.logger.critical('Failed to get training information from previous subjects: %s', traceback.format_exc())
-            training_phase, adaptive_reward = (
-                iblrig.choiceworld.DEFAULT_TRAINING_PHASE, iblrig.choiceworld.DEFAULT_REWARD_VOLUME)
-            self.logger.critical(f'The mouse will train on level {training_phase} and with reward {adaptive_reward} uL')
-
-        return training_phase, adaptive_reward
+            tinfo = dict(
+                training_phase=iblrig.choiceworld.DEFAULT_TRAINING_PHASE,
+                adaptive_reward=iblrig.choiceworld.DEFAULT_REWARD_VOLUME,
+                adaptive_gain=self.task_params.AG_INIT_VALUE
+            )
+            self.logger.critical(f"The mouse will train on level {tinfo['training_phase']}, "
+                                 f"with reward {tinfo['adaptive_reward']} uL and gain {tinfo['adaptive_gain']}")
+        return tinfo['training_phase'], tinfo['adaptive_reward'], tinfo['adaptive_gain']
 
     def compute_performance(self):
         """
@@ -823,7 +880,7 @@ def next_trial(self):
                 # contrast is the last contrast
                 contrast = last_contrast
         # save and send trial info to bonsai
-        self.draw_next_trial_info(pleft=0.5, position=position, contrast=contrast)
+        self.draw_next_trial_info(pleft=self.task_params.PROBABILITY_LEFT, position=position, contrast=contrast)
         self.trials_table.at[self.trial_num, 'training_phase'] = self.training_phase
 
     def show_trial_log(self):

iblrig/base_choice_world_params.yaml

@@ -1,10 +1,5 @@
 'AUTOMATIC_CALIBRATION': true
 'ADAPTIVE_REWARD': false
-'BLOCK_INIT_5050': true
-'BLOCK_LEN_FACTOR': 60
-'BLOCK_LEN_MAX': 100
-'BLOCK_LEN_MIN': 20
-'BLOCK_PROBABILITY_SET': [0.2, 0.8]
 'BONSAI_EDITOR': false
 'CALIBRATION_VALUE': 0.067
 'CONTRAST_SET': [1.0, 0.25, 0.125, 0.0625, 0.0]
@@ -20,17 +15,17 @@
 'ITI_DELAY_SECS': 1
 'NTRIALS': 2000
 'POOP_COUNT': true
+'PROBABILITY_LEFT': 0.5
 'QUIESCENCE_THRESHOLDS': [-2, 2]
 'QUIESCENT_PERIOD': 0.2
 'RECORD_AMBIENT_SENSOR_DATA': true
 'RECORD_SOUND': true
-'REPEAT_ON_ERROR': false
 'RESPONSE_WINDOW': 60
 'REWARD_AMOUNT_UL': 1.5
 'REWARD_TYPE': Water 10% Sucrose
 'STIM_ANGLE': 0.0
 'STIM_FREQ': 0.1
-'STIM_GAIN': 4.0
+'STIM_GAIN': 4.0  # wheel to stimulus relationship (degrees visual angle per mm of wheel displacement)
 'STIM_POSITIONS': [-35, 35]
 'STIM_SIGMA': 7.0
 'STIM_TRANSLATION_Z': 7  # 7 for ephys, 8 otherwise. -p:Stim.TranslationZ-{STIM_TRANSLATION_Z} bonsai parameter

iblrig/base_tasks.py

@@ -11,6 +11,8 @@
 import inspect
 import json
 import os
+from typing import Optional
+
 import serial
 import subprocess
 import time
@@ -47,8 +49,8 @@
 
 class BaseSession(ABC):
     version = None
-    protocol_name = None
-    base_parameters_file = None
+    protocol_name: Optional[str] = None
+    base_parameters_file: Optional[Path] = None
     is_mock = False
     extractor_tasks = None
     checked_for_update = False
@@ -89,23 +91,27 @@ def __init__(self, subject=None, task_parameter_file=None, file_hardware_setting
         self.iblrig_settings = iblrig.path_helper.load_settings_yaml(file_iblrig_settings or 'iblrig_settings.yaml')
         if iblrig_settings is not None:
             self.iblrig_settings.update(iblrig_settings)
+        self.wizard = wizard
         # Load the tasks settings, from the task folder or override with the input argument
-        task_parameter_file = task_parameter_file or Path(inspect.getfile(self.__class__)).parent.joinpath('task_parameters.yaml')
+        base_parameters_files = [
+            task_parameter_file or Path(inspect.getfile(self.__class__)).parent.joinpath('task_parameters.yaml')]
+        # loop through the task hierarchy to gather parameter files
+        for cls in self.__class__.__mro__:
+            base_file = getattr(cls, 'base_parameters_file', None)
+            if base_file is not None:
+                base_parameters_files.append(base_file)
+        # this is a trick to remove list duplicates while preserving order, we want the highest order first
+        base_parameters_files = list(reversed(list(dict.fromkeys(base_parameters_files))))
+        # now we loop into the files and update the dictionary, the latest files in the hierarchy have precedence
         self.task_params = Bunch({})
-        self.wizard = wizard
-
-        # first loads the base parameters for a given task
-        if self.base_parameters_file is not None and self.base_parameters_file.exists():
-            with open(self.base_parameters_file) as fp:
-                self.task_params = Bunch(yaml.safe_load(fp))
-
-        # then updates the dictionary with the child task parameters
-        if task_parameter_file.exists():
-            with open(task_parameter_file) as fp:
-                task_params = yaml.safe_load(fp)
-            if task_params is not None:
-                self.task_params.update(Bunch(task_params))
-
+        for param_file in base_parameters_files:
+            if Path(param_file).exists():
+                with open(param_file) as fp:
+                    params = yaml.safe_load(fp)
+                if params is not None:
+                    self.task_params.update(Bunch(params))
+        # at last sort the dictionary so itś easier for a human to navigate the many keys
+        self.task_params = Bunch(dict(sorted(self.task_params.items())))
         self.session_info = Bunch({
             'NTRIALS': 0,
             'NTRIALS_CORRECT': 0,

iblrig/choiceworld.py

@@ -37,8 +37,8 @@ def compute_adaptive_reward_volume(subject_weight_g, reward_volume_ul, delivered
 
 
 def get_subject_training_info(
-        subject_name, task_name='_iblrig_tasks_trainingChoiceWorld',
-        default_reward=DEFAULT_REWARD_VOLUME, mode='silent', **kwargs) -> tuple[int, float, dict]:
+        subject_name, task_name='_iblrig_tasks_trainingChoiceWorld', stim_gain=None,
+        default_reward=DEFAULT_REWARD_VOLUME, mode='silent', **kwargs) -> tuple[dict, dict]:
     """
     Goes through the history of a subject and gets the latest
     training phase and the adaptive reward volume for this subject
@@ -50,31 +50,38 @@ def get_subject_training_info(
     :param mode: 'defaults' or 'raise': if 'defaults' returns default values if no history is found, if 'raise' raises ValueError
     :param **kwargs: optional arguments to be passed to iblrig.path_helper.get_local_and_remote_paths
     if not used, will use the arguments from iblrig/settings/iblrig_settings.yaml
-    :return: training_phase (int), default_reward uL (float between 1.5 and 3) and a
+    :return: training_info dictionary with keys:
+        default_reward uL (float between 1.5 and 3) and a
     session_info dictionary with keys: session_path, experiment_description, task_settings, file_task_data
     """
     session_info = iterate_previous_sessions(subject_name, task_name=task_name, n=1, **kwargs)
     if len(session_info) == 0:
         if mode == 'silent':
             logger.warning("The training status could not be determined returning default values")
-            return DEFAULT_TRAINING_PHASE, default_reward, None
+            return dict(training_phase=DEFAULT_TRAINING_PHASE, adaptive_reward=default_reward, adaptive_gain=stim_gain), None
         elif mode == 'raise':
             raise ValueError("The training status could not be determined as no previous sessions were found")
     else:
         session_info = session_info[0]
     trials_data, _ = iblrig.raw_data_loaders.load_task_jsonable(session_info.file_task_data)
+    # gets the reward volume from the previous session
     previous_reward_volume = (session_info.task_settings.get('ADAPTIVE_REWARD_AMOUNT_UL') or
                               session_info.task_settings.get('REWARD_AMOUNT_UL'))
     adaptive_reward = compute_adaptive_reward_volume(
         subject_weight_g=session_info.task_settings['SUBJECT_WEIGHT'],
         reward_volume_ul=previous_reward_volume,
         delivered_volume_ul=trials_data['reward_amount'].sum(),
         ntrials=trials_data.shape[0])
+    # gets the trainng_phase by looking at the trials table
     if 'training_phase' in trials_data:
         training_phase = trials_data['training_phase'].values[-1]
     else:
         training_phase = DEFAULT_TRAINING_PHASE
-    return training_phase, adaptive_reward, session_info
+    # gets the adaptive gain
+    adaptive_gain = session_info.task_settings.get('ADAPTIVE_GAIN_VALUE', session_info.task_settings.get('AG_INIT_VALUE'))
+    if np.sum(trials_data['response_side'] != 0) > 200:
+        adaptive_gain = session_info.task_settings.get('STIM_GAIN')
+    return dict(training_phase=training_phase, adaptive_reward=adaptive_reward, adaptive_gain=adaptive_gain), session_info
 
 
 def training_contrasts_probabilities(phase=1):

iblrig/gui/ui_update.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-# Form implementation generated from reading ui file 'iblrig/gui/ui_update.ui'
+# Form implementation generated from reading ui file 'ui_update.ui'
 #
 # Created by: PyQt5 UI code generator 5.15.9
 #
@@ -14,14 +14,14 @@
 class Ui_update(object):
     def setupUi(self, update):
         update.setObjectName("update")
-        update.resize(353, 496)
+        update.resize(451, 496)
         sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed)
         sizePolicy.setHorizontalStretch(0)
         sizePolicy.setVerticalStretch(0)
         sizePolicy.setHeightForWidth(update.sizePolicy().hasHeightForWidth())
         update.setSizePolicy(sizePolicy)
         icon = QtGui.QIcon()
-        icon.addPixmap(QtGui.QPixmap(":/images/wizard.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
+        icon.addPixmap(QtGui.QPixmap("wizard.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
         update.setWindowIcon(icon)
         update.setModal(True)
         self.horizontalLayout_2 = QtWidgets.QHBoxLayout(update)
@@ -32,7 +32,7 @@ def setupUi(self, update):
         self.uiLabelLogo = QtWidgets.QLabel(update)
         self.uiLabelLogo.setMaximumSize(QtCore.QSize(64, 64))
         self.uiLabelLogo.setText("")
-        self.uiLabelLogo.setPixmap(QtGui.QPixmap("iblrig/gui\\wizard.png"))
+        self.uiLabelLogo.setPixmap(QtGui.QPixmap("wizard.png"))
         self.uiLabelLogo.setScaledContents(True)
         self.uiLabelLogo.setObjectName("uiLabelLogo")
         self.uiLayoutLogo.addWidget(self.uiLabelLogo)
@@ -45,10 +45,8 @@ def setupUi(self, update):
         self.uiLabelHeader.setObjectName("uiLabelHeader")
         self.uiLayoutRight.addWidget(self.uiLabelHeader)
         self.uiTextBrowserChanges = QtWidgets.QTextBrowser(update)
-        self.uiTextBrowserChanges.setStyleSheet("")
         self.uiTextBrowserChanges.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOn)
         self.uiTextBrowserChanges.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)
-        self.uiTextBrowserChanges.setDocumentTitle("")
         self.uiTextBrowserChanges.setMarkdown("")
         self.uiTextBrowserChanges.setTextInteractionFlags(QtCore.Qt.NoTextInteraction)
         self.uiTextBrowserChanges.setObjectName("uiTextBrowserChanges")
@@ -96,6 +94,7 @@ def setupUi(self, update):
         self.horizontalLayout_2.setStretch(1, 100)
 
         self.retranslateUi(update)
+        self.uiPushButtonOK.released.connect(update.close) # type: ignore
         QtCore.QMetaObject.connectSlotsByName(update)
 
     def retranslateUi(self, update):