Merge pull request #248 from AllenNeuralDynamics/production_testing

[production merge] 02/13/2024
AllenNeuralDynamics · Feb 13, 2024 · ccd974c · ccd974c
2 parents 87d1fc8 + 25dd86d
commit ccd974c
Show file tree

Hide file tree

Showing 19 changed files with 463 additions and 349 deletions.
diff --git a/README.md b/README.md
@@ -280,46 +280,54 @@ To configure automatic updates consistent with the [update protocol](https://git
 - **Double dipping**: Double dipping statistics in different behavior epochs and conditions.
 
 ### Automatic training
-1. In the main dialog, press `Auto Train` button  <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/9ef26192-044b-4c22-928f-b328b7ab36ab" width="90"> or `Ctrl + Alt + A` to open the Automatic Training dialog
+1. In the main dialog, press `Auto Train` button <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/836a4432-b1b2-4f92-9c66-2441a9d77a82" width="150"> or `Ctrl + Alt + A` to open the Automatic Training dialog
 > [!IMPORTANT]
 > If the dialog fails to open, check AWS credentials at `~/.aws/credentials`. See [instructions](#for-initial-installation)
+
 2. For the first session of a new mouse:
-   - Confirm that this is a new mouse<br>
-   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/bcaafe89-3330-4704-81f9-ab30c259512b" width="400"><br>
-   - On the right side, select a desired curriculum for the new mouse. Double-check `curriculum_name`, `curriculum_version`, and the diagrams<br>.
-   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/2e3c030f-f91e-4917-9fa9-7e27af115171" width="700"><br>
-   - Click buttons to see interative diagrams in browser <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/bb845129-a12b-445a-8639-0e981a60deb9" height="30">
-   - Click `Set curriculum` button <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/f1fac3e0-1c84-42e9-9f12-ba11896845b8" width="60"> to confirm
-   - Now a new entry with `session = 0` is added in `Training history`, and `STAGE_1` of the selected curriculum is suggested by default. <br>
-   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/90a054ab-71c1-4fa2-b13b-ab06f7895080" width="700"><br>
+   1. ‼️ So far, the automatic training system cannot handle our previous Stage 1.1 --> Stage 1.2.<br>
+     For a new mouse, please still use `Coupled Baiting - Stage 1.1` in the "Parameters" page to warm up the mouse as before.<br>
+     Note we always start with `Coupled Baiting - Stage 1.1` even if `curriculum_name == "Uncoupled ..."` (see [this issue](https://github.com/AllenNeuralDynamics/aind-behavior-blog/issues/241))<br>
+     ![image](https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/13c34f43-ceeb-4a79-927e-242f03608602)<br>
+     Once you think the mouse is ready for Stage 1.2, please follow the steps below to start using automatic training. <br>(`Stage 1` in `Auto Train`= `Stage 1.2` in the `Parameters` tab.)<br><br>
+
+   2. Confirm that this is a new mouse in the automatic training system<br>
+   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/684d4aac-f9a9-4ce7-9536-61aace828c76" width="400"><br>
+   3. In "Curriculum Manager", select a desired curriculum for the new mouse. Double-check `curriculum_name` and `curriculum_version`<br>.
+   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/da03961e-71ef-4c6b-a0c0-37475edd1c66" width="700"><br><br>
+   4. (Optional) Click buttons to see interative diagrams in browser <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/99038b93-aef7-48bb-b756-a387bf435a6e" height="30">
+   5. Click `Set curriculum` button <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/2e3f9abd-afda-4a8c-8475-dfb4f2d8065d" width="300"> to confirm
+   6. Now a new entry with `session = 0` is added in `Training history`, and `STAGE_1` of the selected curriculum is suggested by default. <br>
+   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/dac71e11-ed71-4fbc-9c3b-85695aa8ef5c" width="700"><br><br>
 3. For a mouse that already started training
-   - Its training history and curriculum is automatically loaded
-   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/58a9e583-09a4-4295-84eb-db5ef873df5d" width="900">
-   - Uncheck `show this mouse only` to see training history from all mice
+   1. Its training history and curriculum is automatically loaded
+   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/1dc6e13a-a563-425e-81df-28c0e71a8037" width="900">
+   2. (Optional) Uncheck `show this mouse only` to see training history from all mice
    <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/97c05425-7dac-426b-9ba2-aadcfc1868ed" height="30">
-   - Press `Show all training history` <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/ff1354b6-4b78-4740-a317-b5ec1b83686e" height="30"> to open an interactive plotly chart that visualize all training history in browser
-   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/af541948-94ab-4f13-b599-ca188be77324" width="700">
+   3. (Optional) Press `Show auto-training history in Streamlit` <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/c5dc9f2f-8485-47c3-a7e7-ed23947cc4a1" height="30"> to open the Streamlit app showing all training history in browser
+   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/194ee69c-14ec-466d-93b7-3b3e26b4ece8" width="1200">
 4. Apply and lock training parameters
-   - Check the curriculum name and stage name shown on the huge green button<br>
-   <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/c46cd82e-76e7-4d7c-914e-41752937fee8" width="200"><br>
-   - Press the button to apply and lock all curriculum-controlled training parameters in the main GUI (including the "Task").
-   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/27e72b0d-5317-47ba-ac16-7b22e7374cd7" width="900">
-   - Note that you can still modify some items in `Training parameters`, such as `Valve open time`, `Give left/right`, and `Next block`.
-   - You could now close the Auto Training dialog.
-   - Start the training as usual.
-     
+   1. Check the curriculum name and stage name shown on the huge green button<br>
+    <img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/b4bcfd2e-dc68-4349-9208-b06f429c993f" width="200"><br>
+   2. Press the button to apply and lock all curriculum-controlled training parameters in the main GUI (including the "Task").
+   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/fabbf0c2-3f9f-4fa3-8945-030105248851" width="1200">
+   3. Note that you can still modify some items in `Training parameters`, such as `Valve open time`, `Give left/right`, and `Next block`.
+   4. You could now close the Auto Training dialog.
+   5. Start the training as usual.
+
 5. Override parameters (not recommended)
-   - Once `Apply and lock` is pressed, you can press it again to unlock the parameters and override any of them. But in this case, the automatic training mode is disengaged, and this session is considered "off-curriculum".
-
+   1. Once `Apply and lock` is pressed, you can press it again to unlock the parameters and override any of them. But in this case, the automatic training mode is disengaged, and this session is considered "off-curriculum". <br>
+   ![image](https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/08b7997c-bdce-421b-8c90-3a7e4dfc5ba7)
+
 6. Override stage (not recommended)
-   - Check `Override stage` to override the suggested stage. In the example below, `STAGE_FINAL` is suggested, but `STAGE_3` will be actually used (see the green button).
-   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/92fb9df7-3e54-4bf2-a86a-02c3808c554e" width="700">
+   1. Check `Override stage` to override the suggested stage. In the example below, `STAGE_FINAL` is suggested, but `STAGE_3` will be actually used (see the green button).
+   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/3a25ed5c-e92f-4ba4-8fc5-38bd4c498499" width="700">
 
 7. Override curriculum (not recommended)
-     - If you somehow decide to change the curriculum during training, press `Override curriculum` and set a new curriculum.
-     - In this case, since all stages from the old curriculum now become "irrelevant", you should always manually select a stage in the ***new*** curriculum to override.
+   1. If you somehow decide to change the curriculum during training, press `Override curriculum` and set a new curriculum.
+   2. In this case, since all stages from the old curriculum now become "irrelevant", you should always manually select a stage in the ***new*** curriculum to override.
    <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/802e5208-dc5f-45f5-8ece-b9241157bf88" width="300">
-   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/fba62a4b-acdb-49e9-a401-3a382607b0f3" width="700">
+   <br><img src="https://github.com/AllenNeuralDynamics/dynamic-foraging-task/assets/24734299/9f0f0853-8033-46ff-82a6-38b6c01ec136" width="700">
 
 ### Water Calibration
 

diff --git a/src/desktop_shortcuts/BoxA no console.bat b/src/desktop_shortcuts/BoxA no console.bat
@@ -0,0 +1,5 @@
+cd /d C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\foraging_gui
+call conda activate Foraging
+start "" C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\desktop_shortcuts\start_popup.bat
+start "" pythonw Foraging.py 1
+
diff --git a/src/desktop_shortcuts/BoxB no console.bat b/src/desktop_shortcuts/BoxB no console.bat
@@ -0,0 +1,5 @@
+cd /d C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\foraging_gui
+call conda activate Foraging
+start "" C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\desktop_shortcuts\start_popup.bat
+start "" pythonw Foraging.py 2
+
diff --git a/src/desktop_shortcuts/BoxC no console.bat b/src/desktop_shortcuts/BoxC no console.bat
@@ -0,0 +1,5 @@
+cd /d C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\foraging_gui
+call conda activate Foraging
+start "" C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\desktop_shortcuts\start_popup.bat
+start "" pythonw Foraging.py 3
+
diff --git a/src/desktop_shortcuts/BoxD no console.bat b/src/desktop_shortcuts/BoxD no console.bat
@@ -0,0 +1,5 @@
+cd /d C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\foraging_gui
+call conda activate Foraging
+start "" C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\src\desktop_shortcuts\start_popup.bat
+start "" pythonw Foraging.py 4
+
diff --git a/src/desktop_shortcuts/CameraA.bat b/src/desktop_shortcuts/CameraA.bat
@@ -1,3 +1,11 @@
-cd /d C:\Users\svc_aind_behavior\Documents\camera_workflows
-start /min C:\Users\svc_aind_behavior\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxA.bonsai --no-editor 
+cd /d C:\Users\%USERNAME%\Documents\camera_workflows
+echo off
+mode 50,10
+cls
+start /B C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxA.bonsai --no-editor 
+powershell -window minimized -command ""
+timeout 5 > NUL
+title CAMERA A
+echo This window controls camera A
+echo Close this window if camera A is whited out
 
diff --git a/src/desktop_shortcuts/CameraB.bat b/src/desktop_shortcuts/CameraB.bat
@@ -1,3 +1,11 @@
-cd /d C:\Users\svc_aind_behavior\Documents\camera_workflows
-start /min C:\Users\svc_aind_behavior\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxB.bonsai --no-editor 
+cd /d C:\Users\%USERNAME%\Documents\camera_workflows
+echo off
+mode 50,10
+cls
+start /B C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxB.bonsai --no-editor 
+powershell -window minimized -command ""
+timeout 5 > NUL
+title CAMERA B
+echo This window controls camera B
+echo Close this window if camera B is whited out
 
diff --git a/src/desktop_shortcuts/CameraC.bat b/src/desktop_shortcuts/CameraC.bat
@@ -1,3 +1,11 @@
-cd /d C:\Users\svc_aind_behavior\Documents\camera_workflows
-start /min C:\Users\svc_aind_behavior\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxC.bonsai --no-editor 
+cd /d C:\Users\%USERNAME%\Documents\camera_workflows
+echo off
+mode 50,10
+cls
+start /B C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxC.bonsai --no-editor 
+powershell -window minimized -command ""
+timeout 5 > NUL
+title CAMERA C
+echo This window controls camera C
+echo Close this window if camera C is whited out
 
diff --git a/src/desktop_shortcuts/CameraD.bat b/src/desktop_shortcuts/CameraD.bat
@@ -1,3 +1,11 @@
-cd /d C:\Users\svc_aind_behavior\Documents\camera_workflows
-start /min C:\Users\svc_aind_behavior\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxD.bonsai --no-editor 
+cd /d C:\Users\%USERNAME%\Documents\camera_workflows
+echo off
+mode 50,10
+cls
+start /B C:\Users\%USERNAME%\Documents\GitHub\dynamic-foraging-task\bonsai\bonsai Camera_boxD.bonsai --no-editor 
+powershell -window minimized -command ""
+timeout 5 > NUL
+title CAMERA D
+echo This window controls camera D
+echo Close this window if camera D is whited out
 
diff --git a/src/foraging_gui/Dialogs.py b/src/foraging_gui/Dialogs.py
@@ -285,8 +285,8 @@ def _Laser(self,Numb):
                             ItemsRight=sorted(ItemsRight)
                             eval('self.LaserPowerLeft_'+str(Numb)+'.clear()')
                             eval('self.LaserPowerLeft_'+str(Numb)+'.addItems(ItemsLeft)')
-                            eval('self.LaserPowerLeft_'+str(Numb)+'.clear()')
-                            eval('self.LaserPowerLeft_'+str(Numb)+'.addItems(ItemsRight)')
+                            eval('self.LaserPowerRight_'+str(Numb)+'.clear()')
+                            eval('self.LaserPowerRight_'+str(Numb)+'.addItems(ItemsRight)')
                         self.MainWindow.WarningLabel.setText('')
                         self.MainWindow.WarningLabel.setStyleSheet("color: gray;")
                     else:
@@ -334,7 +334,7 @@ def __init__(self, MainWindow,parent=None):
         self.MainWindow=MainWindow
         self.FinishLeftValve=0
         if not hasattr(self.MainWindow,'WaterCalibrationResults'):
-            self.MainWindow.LaserCalibrationResults={}
+            self.MainWindow.WaterCalibrationResults={}
             self.WaterCalibrationResults={}
         else:
             self.WaterCalibrationResults=self.MainWindow.WaterCalibrationResults
@@ -1153,17 +1153,17 @@ def is_file_in_use(file_path):
         except OSError as e:
             return True
 
-class ManipulatorDialog(QDialog):
-    def __init__(self, MainWindow, parent=None):
-        super().__init__(parent)
-        uic.loadUi('Manipulator.ui', self)
+#class ManipulatorDialog(QDialog):
+#    def __init__(self, MainWindow, parent=None):
+#        super().__init__(parent)
+#        uic.loadUi('Manipulator.ui', self)
 
-class MotorStageDialog(QDialog):
-    def __init__(self, MainWindow, parent=None):
-        super().__init__(parent)
-        uic.loadUi('MotorStage.ui', self)
-
-        self.MainWindow=MainWindow
+#class MotorStageDialog(QDialog):
+#    def __init__(self, MainWindow, parent=None):
+#        super().__init__(parent)
+#        uic.loadUi('MotorStage.ui', self)
+#        
+#        self.MainWindow=MainWindow
 
 class LaserCalibrationDialog(QDialog):
     def __init__(self, MainWindow, parent=None):
@@ -1288,6 +1288,8 @@ def _GetLaserWaveForm(self):
         self.CLP_InputVoltage=float(self.voltage.text())
         # generate the waveform based on self.CLP_CurrentDuration and Protocol, Frequency, RampingDown, PulseDur
         self._GetLaserAmplitude()
+        # send the trigger source. It's '/Dev1/PFI0' ( P2.0 of NIdaq USB6002) by default 
+        self.MainWindow.Channel.TriggerSource('/Dev1/PFI0')
         # dimension of self.CurrentLaserAmplitude indicates how many locations do we have
         for i in range(len(self.CurrentLaserAmplitude)):
             # in some cases the other paramters except the amplitude could also be different
@@ -1397,10 +1399,9 @@ def _GetTrainingParameters(self,win):
                 setattr(self, Prefix+'_'+child.objectName(), child.isChecked())
     def _InitiateATrial(self):
         '''Initiate calibration in bonsai'''
-        # send the trigger source. It's '/Dev1/PFI0' ( P2.0 of NIdaq USB6002) by default 
-        self.MainWindow.Channel.TriggerSource('/Dev1/PFI0')
         # start generating waveform in bonsai
         self.MainWindow.Channel.OptogeneticsCalibration(int(1))
+        self.MainWindow.Channel.receive()
     def _CopyFromOpto(self):
         '''Copy the optogenetics parameters'''
         N=[]
@@ -1696,7 +1697,6 @@ def _Open(self):
             # change button color and disable the open button
             self.Open.setEnabled(False)
             self.Open.setStyleSheet("background-color : green;")
-            QApplication.processEvents()
             self._GetTrainingParameters(self.MainWindow)
             self._GetLaserWaveForm()
             self.worker2 = Worker(self._Sleep,float(self.LC_Duration_1)+1)
@@ -1935,7 +1935,7 @@ def _connect_auto_training_manager(self):
         except:
             logger.error("AWS connection failed!")
             QMessageBox.critical(self,
-                                 'Error',
+                                 'Box {}, Error'.format(self.MainWindow.box_letter),
                                  f'AWS connection failed!\n'
                                  f'Please check your AWS credentials at ~\.aws\credentials!')
             return False
@@ -2009,7 +2009,8 @@ def _connect_curriculum_manager(self):
                 bucket='aind-behavior-data',
                 root='foraging_auto_training/saved_curriculums/'
             ),
-            saved_curriculums_local=self.MainWindow.default_saveFolder + '/curriculum_manager/',
+            # saved to tmp folder under user's home directory
+            saved_curriculums_local=os.path.expanduser('~/.aind_auto_train/curriculum_manager/')
         )
 
     def _show_available_curriculums(self):
@@ -2128,7 +2129,7 @@ def _update_stage_to_apply(self):
     def _apply_curriculum(self):
         # Check if a curriculum is selected
         if not hasattr(self, 'selected_curriculum') or self.selected_curriculum is None:
-            QMessageBox.critical(self, "Error", "Please select a curriculum!")
+            QMessageBox.critical(self, "Box {}, Error".format(self.MainWindow.box_letter), "Please select a curriculum!")
             return
 
         # Always enable override stage
@@ -2174,11 +2175,11 @@ def _apply_curriculum(self):
             if self.selected_curriculum['curriculum'] == self.curriculum_in_use:
                 # The selected curriculum is the same as the one in use
                 logger.info(f"Selected curriculum is the same as the one in use. No change is made.")
-                QMessageBox.information(self, "Info", "Selected curriculum is the same as the one in use. No change is made.")
+                QMessageBox.information(self, "Box {}, Info".format(self.MainWindow.box_letter), "Selected curriculum is the same as the one in use. No change is made.")
                 return
             else:
                 # Confirm with the user about overriding the curriculum
-                reply = QMessageBox.question(self, "Confirm",
+                reply = QMessageBox.question(self, "Box {}, Confirm".format(self.MainWindow.box_letter),
                                              f"Are you sure you want to override the curriculum?\n"
                                              f"If yes, please also manually select a training stage.",
                                              QMessageBox.Yes | QMessageBox.No,
@@ -2284,7 +2285,8 @@ def _set_training_parameters(self, paras_dict, if_press_enter=False):
                 task_ind = widget_task.findText(paras_dict['task'])
                 if task_ind < 0:
                     logger.error(f"Task {paras_dict['task']} not found!")
-                    QMessageBox.critical(self, "Error", f'''Task "{paras_dict['task']}" not found. Check the curriculum!''')
+                    QMessageBox.critical(self, "Box {}, Error".format(self.MainWindow.box_letter), 
+                        f'''Task "{paras_dict['task']}" not found. Check the curriculum!''')
                     return [] # Return an empty list without setting anything
                 else:
                     widget_task.setCurrentIndex(task_ind)