Repository: PolarizedLightFieldMicroscopy/napari-LF (language: python)

Issue:
implement & test expanding fields on MacOS
On MacOS, PyQt5 widgets such as the TextEdit do not expand consistent with other components in a layout making the text look cropped. This is primarily because the styling choses the native OS's default layout. This issue is highlighted [here](https://stackoverflow.com/questions/46051480/pyqt5-different-view-on-os-and-linux) and discussed (https://github.com/napari/napari/issues/13). As of now there doesn't seem like a recommended solution from napari.

![image](https://user-images.githubusercontent.com/2894099/215361909-55d637c0-4918-4bf3-a84a-7bd544ef8083.png)

Implement on all layouts.
```
layout().setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
```

https://github.com/PolarizedLightFieldMicroscopy/napari-LF/blob/76f7d2a72c7f55a657f0f6d6fb02ac258bd3d5fd/src/napari_lf/_widgetLF_gui.py#L204
implement & test expanding fields on MacOS
On MacOS, PyQt5 widgets such as the TextEdit do not expand consistent with other components in a layout making the text look cropped. This is primarily because the styling choses the native OS's default layout. This issue is highlighted [here](https://stackoverflow.com/questions/46051480/pyqt5-different-view-on-os-and-linux) and discussed (https://github.com/napari/napari/issues/13). As of now there doesn't seem like a recommended solution from napari.

![image](https://user-images.githubusercontent.com/2894099/215361909-55d637c0-4918-4bf3-a84a-7bd544ef8083.png)

Implement on all layouts.
```
layout().setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
```

https://github.com/PolarizedLightFieldMicroscopy/napari-LF/blob/76f7d2a72c7f55a657f0f6d6fb02ac258bd3d5fd/src/napari_lf/_widgetLF_gui.py#L204
New page for Neural Net processes
#### How enhancement relates to a problem
Having the Neural Net (LFMNet) content and the LF Analyze content viewable simultaneously adds clutter to the screen.

#### Preferred solution
Extract the Neural Net (LFMNet) content onto its own "page".
- [x] Add two buttons under the _Comments_ field: _LF Analyze_ and _Neural Net_.
- [x] When _LF Analyze_ is selected by the user, all the current parameter options are displayed, except for the LFMNet tab. The bottom processing buttons also remain.
- [x] When _Neural Net_ is selected, only fields related to the neural net deconvolution process should be displayed. Keep only the bottom _Deconvolve_ and _Stop_ buttons, with their actions changed accordingly.

Neural Net content (listed in order to appear)
1. some words related to the training process and a relevant link containing more information
2. _Light field image_: field to select the raw/rectified light field image
3. _Model_: field to select training model file
4. _Output image stack_: field to type in the filename desired for the output image
5. _Network model inspector_:  field area that displays content inside the model file selected

#### Alternative solutions considered
The Neural Net page could be its own "subwidget" instead of "page" within the same widget display.

#### Additional context
The rectification process is being included in the process that occurs when the model is applied to the image (#21). 


Discussion / hints:


I still think would be helpful to make the _LF Analyze_ and _Neural Net_ zones separate widgets within our napari-LF plugin. This is the napari built-in method for doing what we are trying to achieve. It is similar to the one button toggle that @rudolfoldenbourg suggested, because it leaves room for additional methods to be added to napari-LF. Below is a screen recording showing two of these widgets within a single plugin ([napari-pyclesperanto-assistant](https://www.napari-hub.org/plugins/napari-pyclesperanto-assistant)) being open and moved around. Widgets can also be moved outside the napari viewer to a different place on your display screen.

https://user-images.githubusercontent.com/23206511/215179079-20b1ce76-17c7-448c-a8c4-3237bd13aaec.mp4


Thanks, Geneva, for illustrating this option. As an alternative, here are two mockups that show how a single button might work a) to switch between two processing modes and b) give a heading to the panels below the button. What is above the button, applies to both modes, what is below is specific to each mode:
![WidgetLayoutLFA](https://user-images.githubusercontent.com/2894530/215197678-c45851a6-7deb-4a4c-a70e-b9e4910bc4a0.jpg)
![WidgetLayoutNN](https://user-images.githubusercontent.com/2894530/215199638-48fb14ff-007f-4b7c-b2f7-5ca13f66c3bd.jpg)

In the Neural Net panel, the first tab Files can be used to setup the deconvolution, the second tab Training can include some narrative about training an NN using a Jupyter notebook, which will be available in the lfa directory of the distribution. A button in that tab might switch to the Jupyter notebook in question (e.g. main_train_neural_net.ipynb).

I like it! @rudolfoldenbourg 
@rudolfoldenbourg @gschlafly 
I'd like to suggest if we are thinking more in line of _cycling_ through the choices does something like below make more sense.. this would make the task of setting the image easy since it does not need to be an image with button _feel_.

![image](https://user-images.githubusercontent.com/2894099/215359951-541844cc-c741-49fc-bcda-bae9bcf32108.png) 

@gschlafly wrt your suggestion of independent widgets, I don't believe the one in the example share any sort of settings between them, like setting the data directory, etc. So in our case we'd have the divide into 3 widget sections.
- Data dir. tab, Misc, Hardware, About (currently merging these together is the plan irrespective of which layout we decide)
- LF Analyze
- Neural Net

I see&ndash;I was hoping that widgets within the same plugin could share some sort of settings. I like your solution for making the _cycling_ through choices more intuitive! @amitabhverma 

Fix patch:
diff --git a/src/napari_lf/_widgetLF.py b/src/napari_lf/_widgetLF.py
index c97db21..61a7279 100644
--- a/src/napari_lf/_widgetLF.py
+++ b/src/napari_lf/_widgetLF.py
@@ -220,28 +220,26 @@ def input_model_change_call():
 					from napari_lf.lfa.neural_nets.LFNeuralNetworkProto import LFNeuralNetworkProto
 				except:
 					from lfa.neural_nets.LFNeuralNetworkProto import LFNeuralNetworkProto
-					
+				
+				# Define input shape, and extract it either from a calib file or the stored checkpoint
+				LFshape = None
 				# Load calib file
 				if self.gui.gui_elms["lfmnet"]["calibration_file"].value == None:
-					return
-				calibFile_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["calibration_file"].value))
-				path = Path(calibFile_path)
-				if path.is_file():
-					import h5py
-					with h5py.File(calibFile_path, "r") as f:
-						lf = f['geometry']
-						LFshape = [lf.attrs['nu'], lf.attrs['nv'], lf.attrs['ns'], lf.attrs['nt']]
-				
-				# LFshape = [lf.nu, lf.nv, lf.ns, lf.nt]
-				# VCDNet
+					pass
+				else:
+					calibFile_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["calibration_file"].value))
+					path = Path(calibFile_path)
+					if path.is_file():
+						import h5py
+						with h5py.File(calibFile_path, "r") as f:
+							lf = f['geometry']
+							LFshape = [lf.attrs['nu'], lf.attrs['nv'], lf.attrs['ns'], lf.attrs['nt']]
+					
 				if self.gui.gui_elms["lfmnet"]["input_model"].value == None:
 					return
 				checkpoint_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["input_model"].value))
-				# LFMNet
-				# checkpoint_path = '../checkpoints/*.ckpt'
-				# Load network based on checkpoint
-				#print(checkpoint_path)
-				#print(LFshape)
+
+				# Load Network
 				net = LFNeuralNetworkProto.load_network_from_file(checkpoint_path, LFshape)
 				
 				# Set network into evaluation mode (faster ode)
@@ -266,13 +264,25 @@ def input_model_change_call():
 		self.gui.populate_cal_img_list()
 		self.gui.load_plugin_prefs()
 		
+		if "mode_choice" in self.gui.settings["main"] and self.gui.settings["main"]["mode_choice"] == 'NeuralNet':
+			self.gui.LFAnalyze_btn_cont.hide()
+			self.gui.NeuralNet_btn_cont.show()
+			self.gui.widget_main_bottom_comps0.hide()
+			self.gui.widget_main_bottom_comps1.hide()
+			self.gui.widget_main_bottom_comps2.show()
+			self.gui._cont_btn_processing.hide()
+			self.gui._cont_btn_processing2.show()
+			self.gui.NeuralNet_btn.toggle()
+		else:
+			self.gui.LFAnalyze_btn.toggle()
+		
 		#Layout
 		layout = QVBoxLayout()
 		self.setLayout(layout)
 		
 		self.setMinimumWidth(480)
 		self.layout().addWidget(self.gui.widget_main_top_comps.native)
-		self.layout().addWidget(self.gui.scroll_bottom)
+		self.layout().addWidget(self.gui.qtab_widget_top)
 		self.layout().addWidget(self.gui.widget_main_proc_btn_comps.native)
 		self.layout().setAlignment(Qt.AlignTop)
 		self.layout().setContentsMargins(0,0,0,0)
@@ -320,12 +330,23 @@ def set_lfa_libs(self):
 				print('LFA could not be loaded from:', self.gui.gui_elms["misc"]["lib_folder"].value)
 				self.gui.gui_elms["misc"]["lib_ver_label"].value = 'Error!'
 				print(traceback.format_exc())
-		
+	
 	def closeEvent(self, event):
 		self.gui.save_plugin_prefs()
+		if self.gui.timer is not None:
+			self.gui.timer.stop()
+		# print('closeEvent')
 			
 	def hideEvent(self, event):
 		self.gui.save_plugin_prefs()
+		if self.gui.timer is not None:
+			self.gui.timer.stop()
+		# print('hideEvent')
+		
+	def showEvent(self, event):
+		if self.gui.timer is not None:
+			self.gui.timer.start(500)
+		# print('showEvent')
 		
 	#Event Filter
 	def eventFilter(self, source, event):
@@ -666,71 +687,90 @@ def run_lf_proj_vol(self, args):
 	def run_lf_net(self, args):
 		try:
 			args += ['--solver','net']
-			gpu_id = 0
+			# torch_device = torch.device("cuda:1")
+			# torch_device = torch.device("cpu:1")
+			gpu_id = 1 #It seems that even when no GPU is selected, devices should be >=1 (Josué Page Vizcaíno)
 			try:
-				gpu_id = self.gui.gpu_choices.index(self.gui.gui_elms["hw"]["gpu_id"].value)
+				gpu_id = max(self.gui.gpu_choices.index(self.gui.gui_elms["hw"]["gpu_id"].value)+1,1)
 			except Exception as err:
 				pass
-			if self.gui.gui_elms["hw"]["disable_gpu"].value == True:
-				args += ['--disable_gpu']
-				args += ['--gpu_id', gpu_id]
-			else:
-				args += ['--gpu_id', gpu_id]
+
 			print(args)
-			print("LFMNet process")
+			print("Neural Net process")
 			
 			print('\t--> hostname:{host}'.format(host=lfdeconvolve.socket.gethostname()))
-			print('\t--> specified gpu-id:{gpuid}'.format(gpuid=gpu_id))
+			
+			if '--disable-gpu' not in args:
+				print('\t--> specified gpu-id:{gpuid}'.format(gpuid=gpu_id))
 			
 			import torch
+			if '--disable-gpu' not in args:
+				print('\t--> cuda available:{gpu}'.format(gpu=torch.cuda.is_available()))
+			print("\t--> device: ","cuda:"+str(gpu_id) if torch.cuda.is_available() and not '--disable-gpu' in args else "cpu")
+			
 			try:
 				from napari_lf.lfa.neural_nets.LFNeuralNetworkProto import LFNeuralNetworkProto
+				from napari_lf.lfa.lflib.lightfield import LightField
 			except:
 				from lfa.neural_nets.LFNeuralNetworkProto import LFNeuralNetworkProto
-			# Load calib file
-			if self.gui.gui_elms["lfmnet"]["calibration_file"].value == None:
-				return
-			
-			calibFile_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["calibration_file"].value))
-			path = Path(calibFile_path)
-			if path.is_file() == False:
-				return
-			# Loadim the calibration data
-			calibration_file = lfdeconvolve.retrieve_calibration_file(calibFile_path, id=str(gpu_id))
-			lfcal = lfdeconvolve.LightFieldCalibration.load(calibration_file)
-			print('\t--> loaded calibration file: %s' % (calibFile_path))
+				from lfa.lflib.lightfield import LightField
+
+
+			cal_present = False
+			# # Load calib file
+			# if self.gui.gui_elms["lfmnet"]["calibration_file"].value != None:
+			# 	calibFile_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["calibration_file"].value))
+			# 	path = Path(calibFile_path)
+			# 	if path.is_file() != False:
+			# 		# Loadim the calibration data
+			# 		calibration_file = lfdeconvolve.retrieve_calibration_file(calibFile_path, id=str(gpu_id))
+			# 		lfcal = lfdeconvolve.LightFieldCalibration.load(calibration_file)
+			# 		print('\t--> loaded calibration file: %s' % (calibFile_path))
+
+			# 	cal_present = True
 			
+			# Check if input file selected
 			if self.gui.gui_elms["lfmnet"]["input_file"].value == None:
 				return
 			LF_File_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["input_file"].value))
+
 			#Import LF image
 			im = lfdeconvolve.load_image(LF_File_path, dtype=lfdeconvolve.np.float32, normalize = False)
 			print('\t--> loaded LF file: %s.  Pixel values range: [%d, %d]' % (LF_File_path, int(im.min()), int(im.max())))
 			
-			# Rectify the image
-			# skip-alignment parameter is set by calib file
-			print('\t--> skip_alignment: %s' % (lfcal.skip_alignment))
-			
-			lf = lfcal.rectify_lf(im)
-			LFshape = [lf.nu, lf.nv, lf.ns, lf.nt]
 			
-			# VCDNet
+			LFshape = None
+			# if cal_present:
+			# 	# Rectify the image
+			# 	# skip-alignment parameter is set by calib file
+			# 	print('\t--> skip_alignment: %s' % (lfcal.skip_alignment))
+			# 	lf = lfcal.rectify_lf(im)
+			# 	LFshape = [lf.nu, lf.nv, lf.ns, lf.nt]
+
+			# Network path present?
 			if self.gui.gui_elms["lfmnet"]["input_model"].value == None:
 				return
 			checkpoint_path = str(os.path.join(str(self.gui.gui_elms["main"]["img_folder"].value), self.gui.gui_elms["lfmnet"]["input_model"].value))
-			# LFMNet
-			# Load network based on checkpoint
 
+
+			# Load network based on checkpoint
 			net = LFNeuralNetworkProto.load_network_from_file(checkpoint_path, LFshape)
 			print('\t--> loaded model-checkpoint file: %s' % (checkpoint_path))
 			
 			# Set network into evaluation mode (faster ode)
 			net.eval()
 			
+			# If there was no calibration file, extract info about lightfield from network
+
+			if not cal_present: # Load LFshape from model
+				LFshape = net.LF_in_shape
+				print(LFshape)
+				lf = LightField(im, LFshape[0], LFshape[1], LFshape[2], LFshape[3],
+                                      representation = LightField.TILED_LENSLET)
 			## Process image:
 			with torch.no_grad():
 				# Move network to device (GPU/CPU)
-				torch_device = torch.device("cuda:"+str(gpu_id) if torch.cuda.is_available() and not '--disable_gpu' in args else "cpu")
+				torch_device = torch.device("cuda:"+str(gpu_id) if torch.cuda.is_available() and not '--disable-gpu' in args else "cpu")
 				net = net.to(torch_device)   
 				# Prepare input to network
 				im_lenslet = lf.asimage(representation = lfdeconvolve.LightField.TILED_LENSLET)
diff --git a/src/napari_lf/_widgetLF_gui.py b/src/napari_lf/_widgetLF_gui.py
index 5e2660b..d86fbda 100644
--- a/src/napari_lf/_widgetLF_gui.py
+++ b/src/napari_lf/_widgetLF_gui.py
@@ -2,7 +2,7 @@
 from pathlib import Path
 from qtpy import QtCore, QtGui
 from qtpy.QtGui import QPixmap, QPainter
-from qtpy.QtCore import Qt, QTimer
+from qtpy.QtCore import Qt, QTimer, QSize
 from qtpy.QtWidgets import *
 from magicgui.widgets import *
 
@@ -35,7 +35,7 @@ def __init__(self):
 		self.logo_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
 		# size is controlled via html label height value, set to 60px
 
-		self.info_label = Label(label=f'<h2><center>LF Analyze</a></center></h2>')
+		self.info_label = Label(label=f'<h2><center>napari-LF</a></center></h2>')
 		dict = LFvals.PLUGIN_ARGS["main"]["img_folder"]
 		self.gui_elms["main"]["img_folder"] = create_widget(dict)
 		
@@ -168,40 +168,142 @@ def __init__(self):
 			self._cont_btn_processing2.native.setStyleSheet("border: 1px dashed white;")
 		
 		_QFormLayout.addRow(self.logo_label.native)
-		_QFormLayout.addRow(self.gui_elms["main"]["img_folder"].label, self.gui_elms["main"]["img_folder"].native)
-		_QFormLayout.addRow(_cont_img_list_btn.native)
-		if self.gui_elms["main"]["metadata_file"].visible:
-			_QFormLayout.addRow(self.gui_elms["main"]["metadata_file"].label, self.gui_elms["main"]["metadata_file"].native)
-			
-
+		
 		self.LFAnalyze_btn = PicButton(QPixmap(LFvals.LFAnalyze_logo_btn_img), QPixmap(LFvals.LFAnalyze_logo_btn_hov_img), QPixmap(LFvals.LFAnalyze_logo_btn_act_img))
+		self.LFAnalyze_btn_cont = Container(name='LFAnalyze_btn', widgets=())
+		self.LFAnalyze_btn_cont.native.layout().addWidget(self.LFAnalyze_btn)
+		
 		self.LFAnalyze_btn.setMaximumHeight(45)
 		self.LFAnalyze_btn.setMaximumHeight(200)
 		# self.LFAnalyze_btn.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
 		self.LFAnalyze_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)
 		self.LFAnalyze_btn.clicked.connect(self.LFAnalyze_btn_call)
-		self.LFAnalyze_btn.toggle()
 		
 		self.NeuralNet_btn = PicButton(QPixmap(LFvals.NeuralNet_logo_btn_img),QPixmap(LFvals.NeuralNet_logo_btn_hov_img),QPixmap(LFvals.NeuralNet_logo_btn_act_img))
+		self.NeuralNet_btn_cont = Container(name='NeuralNet_btn', widgets=())
+		self.NeuralNet_btn_cont.native.layout().addWidget(self.NeuralNet_btn)
+		
 		self.NeuralNet_btn.setMaximumHeight(45)
 		self.NeuralNet_btn.setMaximumHeight(200)
 		# self.NeuralNet_btn.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
 		self.NeuralNet_btn.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)
 		self.NeuralNet_btn.clicked.connect(self.NeuralNet_btn_call)
+		self.NeuralNet_btn_cont.hide()
 		
 		_processing_methods = QWidget()
 		hBoxLayout = QHBoxLayout()
-		hBoxLayout.addWidget(self.LFAnalyze_btn)
-		hBoxLayout.addWidget(self.NeuralNet_btn)
+		hBoxLayout.addWidget(self.LFAnalyze_btn_cont.native)
+		hBoxLayout.addWidget(self.NeuralNet_btn_cont.native)
 		_processing_methods.setLayout(hBoxLayout)
+		_processing_methods.layout().setAlignment(Qt.AlignCenter|Qt.AlignHCenter)
 		_processing_methods.layout().setContentsMargins(0,0,0,0)
-		_QFormLayout.addRow(_processing_methods)
+		_processing_methods.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
+		
+		_QFormLayoutData = QFormLayout()
+		_QFormLayoutData.setContentsMargins(0,0,0,0)
+		_QFormLayoutData.setSpacing(0)
+		_QFormLayoutData.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
+		_widget_data = QWidget()
+		_widget_data.setLayout(_QFormLayoutData)
+		_QFormLayoutData.addRow(self.gui_elms["main"]["img_folder"].label, self.gui_elms["main"]["img_folder"].native)
+		_QFormLayoutData.addRow(_cont_img_list_btn.native)
+		_QFormLayoutData.addRow(_processing_methods)
+		if self.gui_elms["main"]["metadata_file"].visible:
+			_QFormLayoutData.addRow(self.gui_elms["main"]["metadata_file"].label, self.gui_elms["main"]["metadata_file"].native)
+			
+		self.container_data = Container(name='Data', widgets=())
+		self.container_data.native.layout().addWidget(_widget_data)
+		
+		self.qtab_widget_top = QTabWidget()
+		tabBar = TabBar()
+		tabBar.setDrawBase(False)
+		self.qtab_widget_top.setTabBar(tabBar)
+		self.qtab_widget_top.setTabPosition(QTabWidget.West)
+		self.qtab_widget_top.setTabShape(QTabWidget.Triangular)
+		self.qtab_widget_top.setLayout(QFormLayout())
+		self.qtab_widget_top.layout().setContentsMargins(0,0,0,0)
+		self.qtab_widget_top.layout().setSpacing(0)
+		
+		self.data_tab = QWidget()
+		_data_tab_layout = QVBoxLayout()
+		_data_tab_layout.setAlignment(Qt.AlignTop)
+		self.data_tab.setLayout(_data_tab_layout)
+		self.data_tab.layout().addWidget(self.container_data.native)
+		self.qtab_widget_top.addTab(self.data_tab, 'Processing')
+		
+		self.hardware_tab = QWidget()
+		_hardware_tab_layout = QVBoxLayout()
+		_hardware_tab_layout.setAlignment(Qt.AlignTop)
+		self.hardware_tab.setLayout(_hardware_tab_layout)
+		
+		self.qtab_widget_top.addTab(self.hardware_tab, 'Hardware')
+		
+		self.lfa_lib_tab = QWidget()
+		_lfa_lib_tab_layout = QVBoxLayout()
+		_lfa_lib_tab_layout.setAlignment(Qt.AlignTop)
+		self.lfa_lib_tab.setLayout(_lfa_lib_tab_layout)
+		
+		self.qtab_widget_top.addTab(self.lfa_lib_tab, 'Misc')
+		
+		self._about_tab = QWidget()
+		_about_tab_layout = QFormLayout()
+		_about_tab_layout.setAlignment(Qt.AlignTop)
+		_about_tab_layout.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
+		self._about_tab.setLayout(_about_tab_layout)
+		
+		
+		_line = QFrame()
+		_line.setMinimumWidth(1)
+		_line.setFixedHeight(2)
+		_line.setFrameShape(QFrame.HLine)
+		_line.setFrameShadow(QFrame.Sunken)
+		_line.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)
+		_line.setStyleSheet("margin:1px; padding:2px; border:1px solid rgb(128,128,128); border-width: 1px;")
+		
+		_line2 = QFrame()
+		_line2.setMinimumWidth(1)
+		_line2.setFixedHeight(2)
+		_line2.setFrameShape(QFrame.HLine)
+		_line2.setFrameShadow(QFrame.Sunken)
+		_line2.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)
+		_line2.setStyleSheet("margin:1px; padding:2px; border:1px solid rgb(128,128,128); border-width: 1px;")
+		
+		try:
+			from ._version import version as __version__
+		except ImportError:
+			__version__ = "unknown"
+		
+		self.NapariLF_ver_label = Label(value=LFvals.PLUGIN_ARGS['main']['NapariLF_ver_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['NapariLF_ver_label']['help'])
+		self.NapariLF_ver_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
+		self.NapariLF_ver_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
+		
+		self.LFAnalyze_logo_label = Label(value=LFvals.PLUGIN_ARGS['main']['LFAnalyze_logo_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['LFAnalyze_logo_label']['help'])
+		self.LFAnalyze_logo_label.native.setOpenExternalLinks(True)
+		self.LFAnalyze_logo_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
+		self.LFAnalyze_logo_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
+		
+		self.LFMNet_logo_label = Label(value=LFvals.PLUGIN_ARGS['main']['LFMNet_logo_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['LFMNet_logo_label']['help'])
+		self.LFMNet_logo_label.native.setOpenExternalLinks(True)
+		self.LFMNet_logo_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
+		self.LFMNet_logo_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
+		
+		self.NapariLF_ver_label.native.setText(LFvals.PLUGIN_ARGS["main"]["NapariLF_ver_label"]["label"] + __version__)
+		_about_tab_layout.addRow(self.NapariLF_ver_label.native)
+		_about_tab_layout.addRow(_line)
+		_about_tab_layout.addRow(self.LFAnalyze_logo_label.native)
+		_about_tab_layout.addRow(self.LFMNet_logo_label.native)
+		_about_tab_layout.addRow(_line2)
+		self.qtab_widget_top.addTab(self._about_tab, 'About')
+		
+		_QFormLayout.addRow(self.qtab_widget_top)
+
+		
 			
 		# _QFormLayout.addRow(self.gui_elms["main"]["presets"].label, _cont_preset_list_btn.native)
 		# _QFormLayout.addRow(self.gui_elms["main"]["comments"].label, self.commentsArea)
 		
-		_cont_preset_list_btn.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
-		_QFormLayout.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
+		_cont_preset_list_btn.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
+		#_QFormLayout.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 		self.cont_btn_top.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
 		
 		_QFormLayout2 = QFormLayout()
@@ -214,8 +316,6 @@ def __init__(self):
 		self.cont_btn_status_label.native.setStyleSheet("color: rgb(0, 128, 0)")
 		self.cont_btn_status_label.value = ':STATUS: ' + LFvals.PLUGIN_ARGS['main']['status']['value_idle']
 		
-		_QFormLayout2.addRow(self._cont_btn_processing2.native)
-		_QFormLayout2.addRow(self._cont_btn_processing.native)
 		_QFormLayout2.addRow(self.cont_btn_status_label.native)
 		
 		self.cont_btn_status.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
@@ -256,6 +356,7 @@ def __init__(self):
 						if "group" in dict and dict["group"] not in self.groupbox["calibrate"]["required"]:
 							self.groupbox["calibrate"]["required"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_req.append(self.groupbox["calibrate"]["required"][dict["group"]])
 							self.groupbox["calibrate"]["required"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -270,6 +371,7 @@ def __init__(self):
 						else:
 							self.groupbox["calibrate"]["required"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_req.append(self.groupbox["calibrate"]["required"]["misc"])
 							self.groupbox["calibrate"]["required"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -283,6 +385,7 @@ def __init__(self):
 						if "group" in dict and dict["group"] not in self.groupbox["calibrate"]["inspect"]:
 							self.groupbox["calibrate"]["inspect"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_ins.append(self.groupbox["calibrate"]["inspect"][dict["group"]])
 							self.groupbox["calibrate"]["inspect"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -299,6 +402,7 @@ def __init__(self):
 						else:
 							self.groupbox["calibrate"]["inspect"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_ins.append(self.groupbox["calibrate"]["inspect"]["misc"])
 							self.groupbox["calibrate"]["inspect"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -312,6 +416,7 @@ def __init__(self):
 						if "group" in dict and dict["group"] not in self.groupbox["calibrate"]["optional"]:
 							self.groupbox["calibrate"]["optional"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_opt.append(self.groupbox["calibrate"]["optional"][dict["group"]])
 							self.groupbox["calibrate"]["optional"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -326,6 +431,7 @@ def __init__(self):
 						else:
 							self.groupbox["calibrate"]["optional"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_calibrate_opt.append(self.groupbox["calibrate"]["optional"]["misc"])
 							self.groupbox["calibrate"]["optional"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -424,6 +530,7 @@ def btn_cal_opt_defaults():
 						if "group" in dict and dict["group"] not in self.groupbox["rectify"]["required"]:
 							self.groupbox["rectify"]["required"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_rectify_req.append(self.groupbox["rectify"]["required"][dict["group"]])
 							self.groupbox["rectify"]["required"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -441,6 +548,7 @@ def btn_cal_opt_defaults():
 						else:
 							self.groupbox["rectify"]["required"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_rectify_req.append(self.groupbox["rectify"]["required"]["misc"])
 							self.groupbox["rectify"]["required"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -454,6 +562,7 @@ def btn_cal_opt_defaults():
 						if "group" in dict and dict["group"] not in self.groupbox["rectify"]["optional"]:
 							self.groupbox["rectify"]["optional"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_rectify_opt.append(self.groupbox["rectify"]["optional"][dict["group"]])
 							self.groupbox["rectify"]["optional"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -565,6 +674,7 @@ def btn_rec_opt_defaults():
 						if "group" in dict and dict["group"] not in self.groupbox["deconvolve"]["required"]:
 							self.groupbox["deconvolve"]["required"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_deconvolve_req.append(self.groupbox["deconvolve"]["required"][dict["group"]])
 							self.groupbox["deconvolve"]["required"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -582,6 +692,7 @@ def btn_rec_opt_defaults():
 						else:
 							self.groupbox["deconvolve"]["required"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_deconvolve_req.append(self.groupbox["deconvolve"]["required"]["misc"])
 							self.groupbox["deconvolve"]["required"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -595,6 +706,7 @@ def btn_rec_opt_defaults():
 						if "group" in dict and dict["group"] not in self.groupbox["deconvolve"]["optional"]:
 							self.groupbox["deconvolve"]["optional"][dict["group"]] = QGroupBox(dict["group"])
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_deconvolve_opt.append(self.groupbox["deconvolve"]["optional"][dict["group"]])
 							self.groupbox["deconvolve"]["optional"][dict["group"]].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -612,6 +724,7 @@ def btn_rec_opt_defaults():
 						else:
 							self.groupbox["deconvolve"]["optional"]["misc"] = QGroupBox("Misc")
 							vbox = QFormLayout()
+							vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 							_widget_deconvolve_opt.append(self.groupbox["deconvolve"]["optional"]["misc"])
 							self.groupbox["deconvolve"]["optional"]["misc"].setLayout(vbox)
 							if dict["type"] == "bool":
@@ -696,6 +809,7 @@ def btn_dec_opt_defaults():
 			if "group" in dict and dict["group"] not in self.groupbox["projections"] and self.lf_vals["misc"]["group_params"]["value"] == True:
 				self.groupbox["projections"][dict["group"]] = QGroupBox(dict["group"])
 				vbox = QFormLayout()
+				vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 				_widget_projections.append(self.groupbox["projections"][dict["group"]])
 				self.groupbox["projections"][dict["group"]].setLayout(vbox)
 			if "label" not in dict:
@@ -733,6 +847,7 @@ def btn_dec_opt_defaults():
 			if "group" in dict and dict["group"] not in self.groupbox["lfmnet"] and self.lf_vals["misc"]["group_params"]["value"] == True:
 				self.groupbox["lfmnet"][dict["group"]] = QGroupBox(dict["group"])
 				vbox = QFormLayout()
+				vbox.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 				_widget_lfmnet.append(self.groupbox["lfmnet"][dict["group"]])
 				self.groupbox["lfmnet"][dict["group"]].setLayout(vbox)
 			if "label" not in dict:
@@ -812,6 +927,8 @@ def btn_hw_defaults():
 		_widget_hw.append(self.btn_hw_def)
 			
 		self.container_hw = Container(name='HW', widgets=_widget_hw)
+		self.hardware_tab.layout().addWidget(self.container_hw.native)
+		
 			
 		# == MISC ==
 		self.gui_elms["misc"] = {}
@@ -819,6 +936,7 @@ def btn_hw_defaults():
 
 		_misc_widget = QWidget()
 		_layout_misc = QFormLayout(_misc_widget)
+		_layout_misc.setFieldGrowthPolicy(QFormLayout.ExpandingFieldsGrow)
 		_layout_misc.setLabelAlignment(Qt.AlignLeft)
 		_layout_misc.setFormAlignment(Qt.AlignRight)
 		
@@ -902,20 +1020,9 @@ def btn_all_defaults():
 		_layout_misc.addRow(self.btn_all_def.native)
 		
 		self.container_lfa = _misc_widget
+		self.lfa_lib_tab.layout().addWidget(self.container_lfa)
 		
-		self.NapariLF_ver_label = Label(value=LFvals.PLUGIN_ARGS['main']['NapariLF_ver_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['NapariLF_ver_label']['help'])
-		self.NapariLF_ver_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
-		self.NapariLF_ver_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
-		
-		self.LFAnalyze_logo_label = Label(value=LFvals.PLUGIN_ARGS['main']['LFAnalyze_logo_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['LFAnalyze_logo_label']['help'])
-		self.LFAnalyze_logo_label.native.setOpenExternalLinks(True)
-		self.LFAnalyze_logo_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
-		self.LFAnalyze_logo_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
 		
-		self.LFMNet_logo_label = Label(value=LFvals.PLUGIN_ARGS['main']['LFMNet_logo_label']['label'], tooltip=LFvals.PLUGIN_ARGS['main']['LFMNet_logo_label']['help'])
-		self.LFMNet_logo_label.native.setOpenExternalLinks(True)
-		self.LFMNet_logo_label.native.setAlignment(Qt.AlignCenter|Qt.AlignVCenter)
-		self.LFMNet_logo_label.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)
 		
 		# bind values between props
 		# @self.gui_elms["calibrate"]["ulens_focal_length"].changed.connect
@@ -1116,53 +1223,7 @@ def img_folder_changes():
 		self.lfmnet_tab.layout().addWidget(self.container_lfmnet.native)
 		# self.qtab_widget.addTab(self.lfmnet_tab, 'Neural Net')
 		
-		self.hardware_tab = QWidget()
-		_hardware_tab_layout = QVBoxLayout()
-		_hardware_tab_layout.setAlignment(Qt.AlignTop)
-		self.hardware_tab.setLayout(_hardware_tab_layout)
-		self.hardware_tab.layout().addWidget(self.container_hw.native)
-		self.qtab_widget.addTab(self.hardware_tab, 'Hardware')
-		
-		self.lfa_lib_tab = QWidget()
-		_lfa_lib_tab_layout = QVBoxLayout()
-		_lfa_lib_tab_layout.setAlignment(Qt.AlignTop)
-		self.lfa_lib_tab.setLayout(_lfa_lib_tab_layout)
-		self.lfa_lib_tab.layout().addWidget(self.container_lfa)
-		self.qtab_widget.addTab(self.lfa_lib_tab, 'Misc')
-		
-		self._about_tab = QWidget()
-		_about_tab_layout = QFormLayout()
-		_about_tab_layout.setAlignment(Qt.AlignTop)
-		self._about_tab.setLayout(_about_tab_layout)
-		
-		_line = QFrame()
-		_line.setMinimumWidth(1)
-		_line.setFixedHeight(2)
-		_line.setFrameShape(QFrame.HLine)
-		_line.setFrameShadow(QFrame.Sunken)
-		_line.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)
-		_line.setStyleSheet("margin:1px; padding:2px; border:1px solid rgb(128,128,128); border-width: 1px;")
-		
-		_line2 = QFrame()
-		_line2.setMinimumWidth(1)
-		_line2.setFixedHeight(2)
-		_line2.setFrameShape(QFrame.HLine)
-		_line2.setFrameShadow(QFrame.Sunken)
-		_line2.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Minimum)
-		_line2.setStyleSheet("margin:1px; padding:2px; border:1px solid rgb(128,128,128); border-width: 1px;")
 		
-		try:
-			from ._version import version as __version__
-		except ImportError:
-			__version__ = "unknown"
-		
-		self.NapariLF_ver_label.native.setText(LFvals.PLUGIN_ARGS["main"]["NapariLF_ver_label"]["label"] + __version__)
-		_about_tab_layout.addRow(self.NapariLF_ver_label.native)
-		_about_tab_layout.addRow(_line)
-		_about_tab_layout.addRow(self.LFAnalyze_logo_label.native)
-		_about_tab_layout.addRow(self.LFMNet_logo_label.native)
-		_about_tab_layout.addRow(_line2)
-		self.qtab_widget.addTab(self._about_tab, 'About')
 		
 		# self.calib_tab = QWidget()
 		# _calib_tab_layout = QVBoxLayout()
@@ -1176,7 +1237,11 @@ def img_folder_changes():
 		#APP
 		self.widget_main_top_comps = Container(widgets=(), labels=True)
 		self.widget_main_top_comps.native.layout().addWidget(self.cont_btn_top)
-		
+		self.widget_main_top_comps.native.layout().setContentsMargins(0,0,0,0)
+		self.widget_main_top_comps.native.layout().setSpacing(0)
+		if LFvals.dev_true:
+			self.widget_main_top_comps.native.setStyleSheet("border : 1px dashed white;")
+			
 		#self.gui_elms["main"]["comments"].parent.setSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.Fixed)
 
 		vlay = QVBoxLayout()		
@@ -1190,14 +1255,17 @@ def img_folder_changes():
 		# vlay.addStretch()
 		_preset_comments_expand = QWidget()
 		_preset_comments_expand.setLayout(vlay)
+		_preset_comments_expand.layout().setContentsMargins(0,0,0,0)
+		_preset_comments_expand.layout().setSpacing(0)
+		_preset_comments_expand.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
 		
 		self.widget_main_bottom_comps0 = Container(widgets=(), labels=True)
-		self.widget_main_bottom_comps0.native.layout().addWidget(_preset_comments_expand)
+		self.widget_main_bottom_comps0.native.layout().addWidget(_preset_comments_expand, Qt.AlignTop)
 		self.widget_main_bottom_comps0.native.layout().setContentsMargins(0,0,0,0)
-		self.widget_main_top_comps.native.layout().addWidget(self.widget_main_bottom_comps0.native)
-		self.widget_main_top_comps.native.layout().setContentsMargins(0,0,0,0)
-		if LFvals.dev_true:
-			self.widget_main_top_comps.native.setStyleSheet("border : 1px dashed white;")
+		self.widget_main_bottom_comps0.native.layout().setSpacing(0)
+		self.widget_main_bottom_comps0.native.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
+			
+		self.container_data.native.layout().addWidget(self.widget_main_bottom_comps0.native, Qt.AlignTop)
 		
 		self.widget_main_bottom_comps1 = Container(widgets=(), labels=True)
 		self.widget_main_bottom_comps1.native.layout().addWidget(self.qtab_widget)
@@ -1214,6 +1282,19 @@ def img_folder_changes():
 		self.scroll_bottom = QScrollArea()
 		self.scroll_bottom.setWidgetResizable(True)
 		self.scroll_bottom.setWidget(self.widget_main_bottom_comps_scroll.native)
+		self.container_data.native.layout().addWidget(self.scroll_bottom, Qt.AlignTop)
+		
+		_QFormLayout_proc_btns = QFormLayout()
+		_processing_btns = QWidget()
+		_processing_btns.setLayout(_QFormLayout_proc_btns)
+		_QFormLayout_proc_btns.setContentsMargins(0,0,0,0)
+		_QFormLayout_proc_btns.setSpacing(0)
+		_QFormLayout_proc_btns.addRow(self._cont_btn_processing2.native)
+		_QFormLayout_proc_btns.addRow(self._cont_btn_processing.native)
+		_QFormLayout_proc_btns.setAlignment(Qt.AlignBottom)
+		_processing_btns.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
+		
+		self.container_data.native.layout().addWidget(_processing_btns, Qt.AlignBottom)
 		# self.scroll_bottom.setMinimumHeight(400)
 		
 		if LFvals.dev_true:
@@ -1238,13 +1319,20 @@ def LFAnalyze_btn_call(self):
 			self.widget_main_bottom_comps2.visible = False
 			self._cont_btn_processing.visible = True
 			self._cont_btn_processing2.visible = False
+			self.LFAnalyze_btn_cont.visible = True
+			self.NeuralNet_btn_cont.visible = False
+			self.settings["main"]["mode_choice"] = 'LFAnalyze'
 		else:
 			self.widget_main_bottom_comps0.visible = False
 			self.widget_main_bottom_comps1.visible = False
 			self.widget_main_bottom_comps2.visible = True
 			self._cont_btn_processing.visible = False
 			self._cont_btn_processing2.visible = True
+			self.LFAnalyze_btn_cont.visible = False
+			self.NeuralNet_btn_cont.visible = True
+			self.settings["main"]["mode_choice"] = 'NeuralNet'
 		self.NeuralNet_btn.toggle()
+		self.save_plugin_prefs()
 			
 	def NeuralNet_btn_call(self):
 		# print("NeuralNet_btn_call")
@@ -1254,17 +1342,26 @@ def NeuralNet_btn_call(self):
 			self.widget_main_bottom_comps2.visible = True
 			self._cont_btn_processing.visible = False
 			self._cont_btn_processing2.visible = True
+			self.LFAnalyze_btn_cont.visible = False
+			self.NeuralNet_btn_cont.visible = True
+			self.settings["main"]["mode_choice"] = 'NeuralNet'
 		else:
 			self.widget_main_bottom_comps0.visible = True
 			self.widget_main_bottom_comps1.visible = True
 			self.widget_main_bottom_comps2.visible = False
 			self._cont_btn_processing.visible = True
 			self._cont_btn_processing2.visible = False
+			self.LFAnalyze_btn_cont.visible = True
+			self.NeuralNet_btn_cont.visible = False
+			self.settings["main"]["mode_choice"] = 'LFAnalyze'
 		self.LFAnalyze_btn.toggle()
+		self.save_plugin_prefs()
 		
 	def verify_existing_files(self):
 		
 		try:
+			# print('verify_existing_files thread running')
+		
 			_img_folder = str(self.gui_elms["main"]["img_folder"].value)
 			path = Path(_img_folder)
 			if path.is_dir():
@@ -1532,7 +1629,7 @@ def populate_cal_img_list(self):
 		self.gui_elms["rectify"]["calibration_file"].choices = img_files
 		self.gui_elms["deconvolve"]["calibration_file"].choices = img_files
 		self.gui_elms["projections"]["calibration_file"].choices = img_files
-		self.gui_elms["lfmnet"]["calibration_file"].choices = img_files
+		# self.gui_elms["lfmnet"]["calibration_file"].choices = img_files
 		
 	def set_cal_img(self):
 		cal_file = self.gui_elms["calibrate"]["output_filename"].value
@@ -1569,7 +1666,8 @@ def get_PlatForms(self):
 		
 	def save_plugin_prefs(self):
 		for section in LFvals.PLUGIN_ARGS:
-			self.settings[section] = {}
+			if section not in self.settings:
+				self.settings[section] = {}
 			for prop in LFvals.PLUGIN_ARGS[section]:
 				if "exclude_from_settings" in LFvals.PLUGIN_ARGS[section][prop] and LFvals.PLUGIN_ARGS[section][prop]["exclude_from_settings"] == True:
 					pass
@@ -1865,6 +1963,7 @@ def __init__(self, pixmap, pixmap_hover, pixmap_pressed, parent=None):
 		self.pixmap_hover = pixmap_hover
 		self.pixmap_pressed = pixmap_pressed
 		self.setCheckable(True)
+		self.setVisible(True)
 
 	def paintEvent(self, event):
 		pix = self.pixmap_hover if self.underMouse() else self.pixmap
@@ -1881,3 +1980,9 @@ def leaveEvent(self, event):
 
 	def sizeHint(self):
 		return self.pixmap.size()
+
+# https://stackoverflow.com/questions/46007131/make-every-tab-the-same-width-and-also-expandable
+class TabBar(QTabBar):
+	def tabSizeHint(self, index):
+		size = QTabBar.tabSizeHint(self, index)
+		return QSize(size.width(), 100)
\ No newline at end of file
diff --git a/src/napari_lf/_widgetLF_vals.py b/src/napari_lf/_widgetLF_vals.py
index 1839a65..104e970 100644
--- a/src/napari_lf/_widgetLF_vals.py
+++ b/src/napari_lf/_widgetLF_vals.py
@@ -3,19 +3,21 @@
 
 currentdir = os.path.dirname(os.path.realpath(__file__))
 icon_img = os.path.join(currentdir, 'resources/icon.ico')
-logo_img = os.path.join(currentdir, 'resources/napari-LF_logo.png')
+logo_img = os.path.join(currentdir, 'resources/logos/napari-LF_logo.png')
 
 # Image processing logos
+# TODO: Only the active ("act") logos have had their font updated. The other logos
+# 	should be updated. They should also be in used or removed.
 # LF Analyze
 LFAnalyze_logo_img = os.path.join(currentdir, 'resources/LFAnalyze_logo_201_45px.png')
 LFAnalyze_logo_btn_img = os.path.join(currentdir, 'resources/lfa_logo/buttons/half_res/LFA_logo_3D_inact_201_45px.png')
 LFAnalyze_logo_btn_hov_img = os.path.join(currentdir, 'resources/lfa_logo/buttons/half_res/LFA_logo_3D_hov_201_45px.png')
-LFAnalyze_logo_btn_act_img = os.path.join(currentdir, 'resources/lfa_logo/buttons/half_res/LFA_logo_3D_act_201_45px.png')
+LFAnalyze_logo_btn_act_img = os.path.join(currentdir, 'resources/logos/lfa_logo_3d_act.png')
 # Neural Net
 NeuralNet_logo_img = os.path.join(currentdir, 'resources/NeuralNet_logo_201_45px.png')
 NeuralNet_logo_btn_img = os.path.join(currentdir, 'resources/nn_logo/buttons/half_res/NN_logo_3D_inact_201_45px.png')
 NeuralNet_logo_btn_hov_img = os.path.join(currentdir, 'resources/nn_logo/buttons/half_res/NN_logo_3D_hov_201_45px.png')
-NeuralNet_logo_btn_act_img = os.path.join(currentdir, 'resources/nn_logo/buttons/half_res/NN_logo_3D_act_201_45px.png')
+NeuralNet_logo_btn_act_img = os.path.join(currentdir, 'resources/logos/nn_logo_3d_act.png')
 
 loading_img = os.path.join(currentdir, 'resources/loading.gif')
 examples_folder = os.path.join(currentdir, 'examples/antleg')
@@ -55,7 +57,7 @@
 PLUGIN_ARGS = {
 	"main":{
 		"logo_label":{
-			"label":f'<a href="https://chanzuckerberg.com/science/programs-resources/imaging/napari/light-field-imaging-plugin/"><img src="{logo_img}" height="60"></a>',"help":"LF Analyze About WebPage","type":"img_label","default":"","exclude_from_settings":True
+			"label":f'<a href="https://chanzuckerberg.com/science/programs-resources/imaging/napari/light-field-imaging-plugin/"><img src="{logo_img}" height="60"></a>',"help":"napari-LF About WebPage","type":"img_label","default":"","exclude_from_settings":True
 		},
 		"img_folder":{
 			"label":"Project folder","default":examples_folder,"help":"Select your dataset folder containing the raw light field image(s).","type":"folder"
@@ -76,7 +78,7 @@
 			"label":"STATUS:","value":"== IDLE ==","value_busy":"== BUSY ==","value_idle":"== IDLE ==","value_error":"== ERROR ==","type":"label","default":"== IDLE ==","exclude_from_settings":True
 		},
 		"NapariLF_ver_label":{
-			"label":"Napari-LF Plugin Ver:","default":"","help":"Napari-LF Plugin version.","type":"label","exclude_from_settings":True
+			"label":"napari-LF Plugin Ver:","default":"","help":"napari-LF Plugin version.","type":"label","exclude_from_settings":True
 		},
 		"LFAnalyze_logo_label":{
 			"label":f'<a href="https://graphics.stanford.edu/projects/lfmicroscope/"><img src="{LFAnalyze_logo_img}" height="50"></a>',"help":"LF Analyze About WebPage","type":"img_label","default":"","exclude_from_settings":True
@@ -389,9 +391,9 @@
 		"input_file":{
 			"prop":"input_file","label":"Light field image","dest":"input_file","type":"sel","default":"","options":[""],"help":"Supply at least one light field image to rectify.","cat":"required","img_folder_file":True,"group":"Files"
 		},
-		"calibration_file":{
-			"prop":"calibration_file","label":"Calibration file","dest":"calibration_file","type":"sel","default":"","options":[""],"help":"Specify the calibration file to use for rectification.","cat":"required","img_folder_file":True,"group":"Files"
-		},
+		# "calibration_file":{
+		# 	"prop":"calibration_file","label":"Calibration file","dest":"calibration_file","type":"sel","default":"","options":[""],"help":"Specify the calibration file to use for rectification.","cat":"required","img_folder_file":True,"group":"Files"
+		# },
 		"input_model":{
 			"prop":"input_model","label":"Select Model","dest":"input_model","type":"sel","default":"","options":[""],"help":"","exclude_from_args":True,"exclude_from_settings":True,"group":"Files"
 		},
diff --git a/src/napari_lf/lfa/main_train_neural_net.ipynb b/src/napari_lf/lfa/main_train_neural_net.ipynb
index 950976e..b36c0db 100644
--- a/src/napari_lf/lfa/main_train_neural_net.ipynb
+++ b/src/napari_lf/lfa/main_train_neural_net.ipynb
@@ -35,16 +35,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
     "import pytorch_lightning as pl\n",
     "import torch\n",
     "# Let's train a VCDNet. This defines which network we will train\n",
-    "# from neural_nets.VCDNet import VCDNet as NN\n",
+    "from neural_nets.VCDNet import VCDNet as NN\n",
     "# Or:\n",
-    "from neural_nets.LFMNet import LFMNet as NN"
+    "# from neural_nets.LFMNet import LFMNet as NN"
    ]
   },
   {
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,16 +72,15 @@
     "# Learning rate\n",
     "training_settings['lr'] = 1e-3    \n",
     "# Batch size              \n",
-    "training_settings['batch_size'] = 2  \n",
+    "training_settings['batch_size'] = 1\n",
     "# max epochs to train        \n",
-    "training_settings['epochs'] = 5\n",
+    "training_settings['epochs'] = 2\n",
     "# Which image-volume pairs to use\n",
-    "training_settings['images_ids'] = list(range(10))                                               \n",
+    "training_settings['images_ids'] = list(range(5))                                               \n",
     "# Where is the data\n",
-    "training_settings['dataset_path'] = 'D:/BrainImagesJosuePage/Brain_40x_64Depths_362imgs.h5'    \n",
+    "training_settings['dataset_path'] = '/Users/page/Documents/PhD/Datasets/Brain_40x_64Depths_362imgs.h5'    \n",
     "# Where to store the trained network?\n",
-    "# training_settings['output_dir'] = 'C:/Users/OldenbourgLab2/Code/napari-LF-neural_nets/examples/pretrained_networks/'    # If left blank the logs and trained network are stored at ./lightning_logs/version_*\n",
-    "training_settings['output_dir'] = 'C:/Users/OldenbourgLab2/Desktop'\n",
+    "training_settings['output_dir'] = '/Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs/'\n",
     "# Where to store the trained network?\n",
     "training_settings['output_prefix'] = ''    # if left blank incremental versioning will be used 'version_*'"
    ]
@@ -97,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,32 +123,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
     {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] Unable to open file (unable to open file: name = 'D:/BrainImagesJosuePage/Brain_40x_64Depths_362imgs.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m net\u001b[39m.\u001b[39;49mconfigure_dataloader()\n",
-      "File \u001b[0;32m~/Documents/PhD/MBL_visit/Projects/napari-LF/src/napari_lf/lfa/neural_nets/LFMNet.py:57\u001b[0m, in \u001b[0;36mLFMNet.configure_dataloader\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdefault_training_settings \u001b[39m=\u001b[39m {\u001b[39m'\u001b[39m\u001b[39mepochs\u001b[39m\u001b[39m'\u001b[39m              : \u001b[39m3\u001b[39m, \n\u001b[1;32m     46\u001b[0m                                   \u001b[39m'\u001b[39m\u001b[39mimages_ids\u001b[39m\u001b[39m'\u001b[39m          : \u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m10\u001b[39m,\u001b[39m15\u001b[39m)), \n\u001b[1;32m     47\u001b[0m                                   \u001b[39m'\u001b[39m\u001b[39mbatch_size\u001b[39m\u001b[39m'\u001b[39m          : \u001b[39m1\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     53\u001b[0m                                   \u001b[39m'\u001b[39m\u001b[39mdataset_path\u001b[39m\u001b[39m'\u001b[39m        : \u001b[39m'\u001b[39m\u001b[39mD:\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mBrainImagesJosuePage\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mBrain_40x_64Depths_362imgs.h5\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m     54\u001b[0m                                   \u001b[39m'\u001b[39m\u001b[39moutput_dir\u001b[39m\u001b[39m'\u001b[39m       : \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m}\n\u001b[1;32m     56\u001b[0m \u001b[39m# Load data\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m all_data \u001b[39m=\u001b[39m Dataset(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_train_setting(\u001b[39m'\u001b[39;49m\u001b[39mdataset_path\u001b[39;49m\u001b[39m'\u001b[39;49m), \u001b[39m261290\u001b[39;49m, \\\n\u001b[1;32m     58\u001b[0m                 fov\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mLF_in_fov, \n\u001b[1;32m     59\u001b[0m                 neighShape\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_train_setting(\u001b[39m'\u001b[39;49m\u001b[39mLF_ROI_size\u001b[39;49m\u001b[39m'\u001b[39;49m), \n\u001b[1;32m     60\u001b[0m                 img_indices\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_train_setting(\u001b[39m'\u001b[39;49m\u001b[39mimages_ids\u001b[39;49m\u001b[39m'\u001b[39;49m), \n\u001b[1;32m     61\u001b[0m                 get_full_imgs\u001b[39m=\u001b[39;49m\u001b[39mnot\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_train_setting(\u001b[39m'\u001b[39;49m\u001b[39muse_patches\u001b[39;49m\u001b[39m'\u001b[39;49m), center_region\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m     63\u001b[0m \u001b[39m# Create train and test loaders\u001b[39;00m\n\u001b[1;32m     64\u001b[0m train_size \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m((\u001b[39m1\u001b[39m \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_train_setting(\u001b[39m'\u001b[39m\u001b[39mvalidation_split\u001b[39m\u001b[39m'\u001b[39m)) \u001b[39m*\u001b[39m \u001b[39mlen\u001b[39m(all_data))\n",
-      "File \u001b[0;32m~/Documents/PhD/MBL_visit/Projects/napari-LF/src/napari_lf/lfa/neural_nets/util/LFUtil.py:15\u001b[0m, in \u001b[0;36mDataset.__init__\u001b[0;34m(self, fname, randomSeed, img_indices, fov, neighShape, keep_imgs, random_imgs, center_region, get_full_imgs)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, fname, randomSeed\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, img_indices\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, fov\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \\\n\u001b[1;32m     14\u001b[0m      neighShape\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, keep_imgs\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, random_imgs\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, center_region\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, get_full_imgs\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m---> 15\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mh5_container \u001b[39m=\u001b[39m h5py\u001b[39m.\u001b[39;49mFile(fname, \u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m     16\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mneighShape \u001b[39m=\u001b[39m neighShape\n\u001b[1;32m     17\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mLFShape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mh5_container[\u001b[39m'\u001b[39m\u001b[39mLFData\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mshape\n",
-      "File \u001b[0;32m~/miniconda/envs/napari-env/lib/python3.9/site-packages/h5py/_hl/files.py:567\u001b[0m, in \u001b[0;36mFile.__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, meta_block_size, **kwds)\u001b[0m\n\u001b[1;32m    558\u001b[0m     fapl \u001b[39m=\u001b[39m make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,\n\u001b[1;32m    559\u001b[0m                      locking, page_buf_size, min_meta_keep, min_raw_keep,\n\u001b[1;32m    560\u001b[0m                      alignment_threshold\u001b[39m=\u001b[39malignment_threshold,\n\u001b[1;32m    561\u001b[0m                      alignment_interval\u001b[39m=\u001b[39malignment_interval,\n\u001b[1;32m    562\u001b[0m                      meta_block_size\u001b[39m=\u001b[39mmeta_block_size,\n\u001b[1;32m    563\u001b[0m                      \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[1;32m    564\u001b[0m     fcpl \u001b[39m=\u001b[39m make_fcpl(track_order\u001b[39m=\u001b[39mtrack_order, fs_strategy\u001b[39m=\u001b[39mfs_strategy,\n\u001b[1;32m    565\u001b[0m                      fs_persist\u001b[39m=\u001b[39mfs_persist, fs_threshold\u001b[39m=\u001b[39mfs_threshold,\n\u001b[1;32m    566\u001b[0m                      fs_page_size\u001b[39m=\u001b[39mfs_page_size)\n\u001b[0;32m--> 567\u001b[0m     fid \u001b[39m=\u001b[39m make_fid(name, mode, userblock_size, fapl, fcpl, swmr\u001b[39m=\u001b[39;49mswmr)\n\u001b[1;32m    569\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(libver, \u001b[39mtuple\u001b[39m):\n\u001b[1;32m    570\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_libver \u001b[39m=\u001b[39m libver\n",
-      "File \u001b[0;32m~/miniconda/envs/napari-env/lib/python3.9/site-packages/h5py/_hl/files.py:231\u001b[0m, in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[39mif\u001b[39;00m swmr \u001b[39mand\u001b[39;00m swmr_support:\n\u001b[1;32m    230\u001b[0m         flags \u001b[39m|\u001b[39m\u001b[39m=\u001b[39m h5f\u001b[39m.\u001b[39mACC_SWMR_READ\n\u001b[0;32m--> 231\u001b[0m     fid \u001b[39m=\u001b[39m h5f\u001b[39m.\u001b[39;49mopen(name, flags, fapl\u001b[39m=\u001b[39;49mfapl)\n\u001b[1;32m    232\u001b[0m \u001b[39melif\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mr+\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m    233\u001b[0m     fid \u001b[39m=\u001b[39m h5f\u001b[39m.\u001b[39mopen(name, h5f\u001b[39m.\u001b[39mACC_RDWR, fapl\u001b[39m=\u001b[39mfapl)\n",
-      "File \u001b[0;32mh5py/_objects.pyx:54\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mh5py/_objects.pyx:55\u001b[0m, in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mh5py/h5f.pyx:106\u001b[0m, in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] Unable to open file (unable to open file: name = 'D:/BrainImagesJosuePage/Brain_40x_64Depths_362imgs.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading images: 100%|██████████| 5/5 [00:08<00:00,  1.65s/it]\n"
      ]
     }
    ],
    "source": [
-    "net.configure_dataloader()"
+    "net.configure_dataloader()\n",
+    "# img_out = LF2Spatial(net.train_loader.dataset[0][0].unsqueeze(0), LFshape)\n",
+    "# import lfdeconvolve\n",
+    "# lfdeconvolve.save_image('./img_out.tif', img_out[0][0].numpy())"
    ]
   },
   {
@@ -168,23 +159,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Missing logger folder: /Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs/LFMNet\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "###################### Logging to: /Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs/LFMNet\n",
+      "###################### Logging to: /Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs//VCDNet\n",
       "run in the terminal to view progress:\n",
-      "tensorboard --logdir=/Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs/LFMNet/version_0\n"
+      "tensorboard --logdir=/Users/page/Documents/PhD/MBL_visit/Projects/napari-LF/runs//VCDNet/version_2\n"
      ]
     }
    ],
@@ -215,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -228,18 +212,17 @@
       "TPU available: False, using: 0 TPU cores\n",
       "IPU available: False, using: 0 IPUs\n",
       "HPU available: False, using: 0 HPUs\n",
-      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/setup.py:200: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
-      "  rank_zero_warn(\n",
       "\n",
-      "  | Name               | Type       | Params\n",
-      "--------------------------------------------------\n",
-      "0 | lensletConvolution | Sequential | 1.5 K \n",
-      "1 | Unet               | UNetLF     | 24.8 K\n",
-      "--------------------------------------------------\n",
-      "26.2 K    Trainable params\n",
+      "  | Name           | Type       | Params\n",
+      "----------------------------------------------\n",
+      "0 | net_upsample   | Sequential | 6.9 M \n",
+      "1 | encoder_layers | Sequential | 3.9 M \n",
+      "2 | decoder_layers | Sequential | 9.4 M \n",
+      "----------------------------------------------\n",
+      "20.2 M    Trainable params\n",
       "0         Non-trainable params\n",
-      "26.2 K    Total params\n",
-      "0.105     Total estimated model params size (MB)\n"
+      "20.2 M    Total params\n",
+      "80.707    Total estimated model params size (MB)\n"
      ]
     },
     {
@@ -247,14 +230,14 @@
      "output_type": "stream",
      "text": [
       "Using device: cpu\n",
-      "Sanity Checking DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]"
+      "                                                                           "
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
+      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1600: PossibleUserWarning: The number of training batches (4) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
       "  rank_zero_warn(\n"
      ]
     },
@@ -262,32 +245,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                                                           "
+      "Epoch 1: 100%|██████████| 5/5 [00:36<00:00,  7.31s/it, loss=0.0592, v_num=1]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
-      "  rank_zero_warn(\n",
-      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1600: PossibleUserWarning: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
-      "  rank_zero_warn(\n"
+      "`Trainer.fit` stopped: `max_epochs=2` reached.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 0:  33%|███▎      | 2/6 [00:47<01:34, 23.69s/it, loss=0.233, v_num=0]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/page/miniconda/envs/napari-env/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py:48: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
-      "  rank_zero_warn(\"Detected KeyboardInterrupt, attempting graceful shutdown...\")\n"
+      "Epoch 1: 100%|██████████| 5/5 [00:36<00:00,  7.39s/it, loss=0.0592, v_num=1]\n"
      ]
     }
    ],
@@ -313,20 +285,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "ename": "RuntimeError",
-     "evalue": "shape '[1, 1, 1287, 1287]' is invalid for input of size 2405601",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn [7], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m pred_vol \u001b[38;5;241m=\u001b[39m net(LF_img_4D\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m))\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# Compute 2D lenslet image for display\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m LF_img_lenslet \u001b[38;5;241m=\u001b[39m \u001b[43mLF2Spatial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mLF_img_4D\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLFshape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Compute volume Maximum intensity projection\u001b[39;00m\n\u001b[0;32m     11\u001b[0m vol_MIP \u001b[38;5;241m=\u001b[39m volume_2_projections(pred_vol)\n",
-      "File \u001b[1;32m~\\Documents\\GitHub\\napari-LF\\src\\napari_lf\\lfa\\neural_nets\\util\\LFUtil.py:161\u001b[0m, in \u001b[0;36mLF2Spatial\u001b[1;34m(xIn, LFSize)\u001b[0m\n\u001b[0;32m    159\u001b[0m x \u001b[38;5;241m=\u001b[39m xIn\n\u001b[0;32m    160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xIn\u001b[38;5;241m.\u001b[39mndimension() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m6\u001b[39m:\n\u001b[1;32m--> 161\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mxIn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpermute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontiguous\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxShape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxShape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLFSize\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mLFSize\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mLFSize\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mLFSize\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m xIn\u001b[38;5;241m.\u001b[39mndimension() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[0;32m    163\u001b[0m     x \u001b[38;5;241m=\u001b[39m xIn\u001b[38;5;241m.\u001b[39mview(xShape[\u001b[38;5;241m0\u001b[39m],xShape[\u001b[38;5;241m1\u001b[39m],LFSize[\u001b[38;5;241m2\u001b[39m],LFSize[\u001b[38;5;241m0\u001b[39m],LFSize[\u001b[38;5;241m3\u001b[39m],LFSize[\u001b[38;5;241m1\u001b[39m])\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n",
-      "\u001b[1;31mRuntimeError\u001b[0m: shape '[1, 1, 1287, 1287]' is invalid for input of size 2405601"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0:  17%|█▋        | 1/6 [48:45<4:03:49, 2925.95s/it, loss=0.167, v_num=0]\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
diff --git a/src/napari_lf/lfa/neural_nets/LFNeuralNetworkProto.py b/src/napari_lf/lfa/neural_nets/LFNeuralNetworkProto.py
index c7d19f8..a7b3184 100644
--- a/src/napari_lf/lfa/neural_nets/LFNeuralNetworkProto.py
+++ b/src/napari_lf/lfa/neural_nets/LFNeuralNetworkProto.py
@@ -102,7 +102,7 @@ def configure_optimizers(self):
     
 ############## Load a network from a checkpoint? Here
     @staticmethod
-    def load_network_from_file(file_name, LFshape):
+    def load_network_from_file(file_name, LFshape=None):
 
         # Extract which network to use from checkpoint file
         checkpoint_file = glob.glob(file_name)
@@ -129,7 +129,10 @@ def load_network_from_file(file_name, LFshape):
         network_class = getattr(mod, network_name)
         
         # Check that hyperparameters and input data matches
-        assert np.all([network_hp['network_settings_dict']['LFshape'], LFshape]), f"Light field shape missmatch: Network used {network_hp['network_settings_dict']['LFshape']} and user provided {LFshape}"
+        if LFshape is not None:
+            assert np.all([network_hp['network_settings_dict']['LFshape'], LFshape]), f"Light field shape missmatch: Network used {network_hp['network_settings_dict']['LFshape']} and user provided {LFshape}"
+        else:
+            LFshape = network_hp['network_settings_dict']['LFshape']
         # Update input image shape
         network_hp['input_shape'] = [LFshape[0]*LFshape[2], LFshape[1]*LFshape[3]]
         # Create network with stored hyperparameters
diff --git a/src/napari_lf/napari.yaml b/src/napari_lf/napari.yaml
index 1f3056e..ed5d72a 100644
--- a/src/napari_lf/napari.yaml
+++ b/src/napari_lf/napari.yaml
@@ -43,7 +43,7 @@ contributions:
       key: unique_id.1 
   widgets:
     - command: napari-LF.make_lfqwidget
-      display_name: napari LF
+      display_name: Main Menu
     # - command: napari-LF.make_qwidget
       # display_name: Example QWidget
     # - command: napari-LF.make_magic_widget
diff --git a/src/napari_lf/resources/logos/lfa_logo_3d_act.png b/src/napari_lf/resources/logos/lfa_logo_3d_act.png
new file mode 100644
index 0000000..aec499e
Binary files /dev/null and b/src/napari_lf/resources/logos/lfa_logo_3d_act.png differ
diff --git a/src/napari_lf/resources/logos/napari-LF_logo.png b/src/napari_lf/resources/logos/napari-LF_logo.png
new file mode 100644
index 0000000..8594b62
Binary files /dev/null and b/src/napari_lf/resources/logos/napari-LF_logo.png differ
diff --git a/src/napari_lf/resources/logos/nn_logo_3d_act.png b/src/napari_lf/resources/logos/nn_logo_3d_act.png
new file mode 100644
index 0000000..25d7f09
Binary files /dev/null and b/src/napari_lf/resources/logos/nn_logo_3d_act.png differ


Test patch:
diff --git a/src/napari_lf/_widgetLF_test.py b/src/napari_lf/_widgetLF_test.py
new file mode 100644
index 0000000..64d0895
--- /dev/null
+++ b/src/napari_lf/_widgetLF_test.py
@@ -0,0 +1,16 @@
+from qtpy.QtWidgets import *
+
+# Fix for the QPixMap error
+# https://github.com/PolarizedLightFieldMicroscopy/napari-LF/issues/29
+app = QApplication([])
+
+try:
+	from napari_lf import _widgetLF as napariLF
+except:
+	import _widgetLF as napariLF
+	
+METHODS = ['PLUGIN','NAPARI','APP']
+METHOD = METHODS[0]
+
+if __name__ == "__main__":
+	napariLF.main(METHODS[1])
\ No newline at end of file