update

app.py

@@ -9,46 +9,76 @@ gradio_temp_dir = os.path.join(tempfile.gettempdir(), 'gradio')
 os.makedirs(gradio_temp_dir, exist_ok=True)
 os.environ['GRADIO_TEMP_DIR'] = gradio_temp_dir
 
+
+title = """
+<div>
+	<div style="display: flex; justify-content: center; text-align: center; font-size: 2rem;">
+		<b>Artifact Removal Transformer 🤗 Gradio Demo</b>
+	</div>
+	<br>
+	<div style="display: flex; justify-content: center; text-align: center;">
+		<p>
+		<b>ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals</b>
+		<br>
+		Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Anne-Mei Bessas
+		</p>
+	</div>
+	<br>
+	<div style="display: flex; justify-content: center; column-gap: 4px;">
+		<a href='https://arxiv.org/abs/2409.07326' target='_blank'">
+			<img src='https://img.shields.io/badge/paper-arXiv-red'>
+		</a>
+		<a href='https://github.com/CNElab-Plus/ArtifactRemovalTransformer' target='_blank'>
+			<img src='https://img.shields.io/badge/code-GitHub-blue'>
+		</a>
+		<a href='https://sites.google.com/view/chchuang' target='_blank'>
+			<img src='https://img.shields.io/badge/website-CNElab-blueviolet'>
+		</a>
+	</div>
+</div>
+"""
+
 guide = """
 
-This tool is designed to assist you with two main tasks:
+This 🤗 Gradio Demo is designed to assist you with two main tasks:
 1. **Channel Mapping**: Align your EEG channels with our template channels to ensure compatibility with our models.
 2. **EEG Artifact Removal**: Use our models—**ART**, **IC-U-Net**, **IC-U-Net++**, and **IC-U-Net-Attn**—to denoise your EEG data.
 
 ## File Requirements and Preparation
 - **Channel locations**: If you don't have the channel location file, we recommend you to download the standard montage <a href="">here</a>. If the channels in those files don't match yours, you can use **EEGLAB** to adjust them to your required montage.
-- **Raw data**: Your data format must be a two-dimensional array (channels, timepoints).
-- **Channel requirements**: Your data must include some channels that correspond to our template channels, which include: **Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T7, C3, Cz, C4, T8, TP7, CP3, CPz, CP4, TP8, P7, P3, Pz, P4, P8, O1, Oz, O2**. At least some of them need to be present for successful mapping. Additionally, please remove any reference, ECG, EOG, EMG, or other non-EEG channels before uploading your files.
+- **Raw data**: Your data format must be a two-dimensional array (channels, timepoints).<br>
+❗️❗️❗️**Channel requirements**: Your data must include some channels that correspond to our template channels, which include: **Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T7, C3, Cz, C4, T8, TP7, CP3, CPz, CP4, TP8, P7, P3, Pz, P4, P8, O1, Oz, O2**. At least some of them need to be present for successful mapping. Additionally, please remove any reference, ECG, EOG, EMG, or other non-EEG channels before uploading your files.
 
-## 1. Channel Mapping
+## Step1. Channel Mapping
 The following steps will guide you through the process of mapping your EEG channels to our template channels.
 
-### Step1: Initial Matching and Scaling
-After clicking on ``Map`` button, we will first match your channels to our template channels by their names. Using the matched channels as reference points, we will apply Thin Plate Spline (TPS) transformation to scale your montage to align with our template's dimensions. The template montage and your scaled montage will be displayed side by side for comparison. Channels that do not have a match in our template will be **highlighted in red**.
+### Step1-1: Initial Matching and Scaling
+After clicking on `Map` button, we will first match your channels to our template channels by their names. Using the matched channels as reference points, we will apply Thin Plate Spline (TPS) transformation to scale your montage to align with our template's dimensions. The template montage and your scaled montage will be displayed side by side for comparison. Channels that do not have a match in our template will be **highlighted in red**.
 - If your data includes all the 30 template channels, you will be directed to **Mapping Result**.
 - If your data doesn't include all the 30 template channels and you have some channels that do not match the template, you will be directed to **Step2**.
 - If all your channels are included in our template but you have fewer than 30 channels, you will be directed to **Step3**.
 
-### Step2: Forwarding Unmatched Channels
-In this step, you will handle the channels that didn't have a direct match with our template, by manually assigning them to the template channels that are still empty, ensuring the most efficient use of your data.  
+### Step1-2: Forwarding Unmatched Channels
+In this step, you will handle the channels that didn't have a direct match with our template, by manually assigning them to the template channels that are still empty, ensuring the most efficient use of your data.<br>
 Your unmatched channels, previously highlighted in red, will be shown on your montage with a radio button displayed above each. You can choose to forward the data from these unmatched channels to the empty template channels. The interface will display each empty template channel in sequence, allowing you to select which of your unmatched channels to forward.
 - If all empty template channels are filled by your selections, you will be directed to **Mapping Result**.
 - If there are still empty template channels remaining, you will be directed to **Step3**.
 
-### Step3: Filling Remaining Template Channels
+### Step1-3: Filling Remaining Template Channels
 To run the models successfully, we need to ensure that all 30 template channels are filled. In this step, you are required to select one of the methods provided below to fill the remaining empty template channels:
 - **Mean** method: Each empty template channel is filled with the average value of data from the nearest input channels. By default, the 4 closest input channels (determined after aligning your montage to the template's scale using TPS) are selected for this averaging process. On the interface, you will see checkboxes displayed above each of your channel. The 4 nearest channels are pre-selected by default for each empty template channel, but you can modify these selections as needed. If you uncheck all the checkboxes for a particular template channel, it will be filled with zeros.
-- **Zero** method: All empty template channels are filled with zeros.  
-Choose the method that best suits your needs, considering that the model's performance may vary depending on the method used.  
+- **Zero** method: All empty template channels are filled with zeros.<br>
+Choose the method that best suits your needs, considering that the model's performance may vary depending on the method used.<br>
 Once all template channels are filled, you will be directed to **Mapping Result**.
 
 ### Mapping Result
 After completing the previous steps, your channels will be aligned with the template channels required by our models.
 - In case there are still some channels that haven't been mapped, we will automatically batch and optimally assign them to the template. This ensures that even channels not initially mapped will still be included in the final result.
-- Once the mapping process is completed, a JSON file containing the mapping result will be generated. This file is necessary only if you plan to run the models using the <a href="https://github.com/CNElab-Plus/ArtifactRemovalTransformer">source code</a>; otherwise, you can ignore it.
+- Once the mapping process is completed, a JSON file containing the mapping result will be generated. This file is necessary only if you plan to run the models using the source code; otherwise, you can ignore it.
 
-## 2. Data Denoising
-After clicking on ``Run`` button, we will process your EEG data based on the mapping result. If necessary, your data will be divided into batches and run the models on each batch sequentially, ensuring that all channels are properly processed.
+## Step2. Data Denoising
+After uploading your EEG data and clicking on `Run` button, we will process your data based on the mapping result.<br>
+(If necessary, your data will be divided into batches and run the models on each batch sequentially, ensuring that all channels are properly processed.)
 """
 
 icunet = """
@@ -86,11 +116,11 @@ init_js = """
 	
 	let selector, attribute;
 	if(stage1_info.state == "step2-selecting"){
-	    selector = "#radio-group > div:nth-of-type(2)";
-	    attribute = "value";
+		selector = "#radio-group > div:nth-of-type(2)";
+		attribute = "value";
 	}else if(stage1_info.state == "step3-2-selecting"){
-	    selector = "#chkbox-group > div:nth-of-type(2)";
-	    attribute = "name";
+		selector = "#chkbox-group > div:nth-of-type(2)";
+		attribute = "name";
 	}else return;
 	
 	const div = document.querySelector(selector);
@@ -169,10 +199,10 @@ update_js = """
 	let selector;
 	let cnt, name, left, bottom;
 	if(stage1_info.state == "step2-selecting"){
-	    selector = "#radio-group > div:nth-of-type(2)";
-	    cnt = stage1_info.step2.count;
-	    
-	    // update the radios
+		selector = "#radio-group > div:nth-of-type(2)";
+		cnt = stage1_info.step2.count;
+		
+		// update the radios
 		const elements = document.querySelectorAll(selector+" > label");
 		Array.from(elements).forEach( el => {
 			name = el.querySelector(":scope > input").value;
@@ -181,8 +211,8 @@ update_js = """
 			el.style.cssText = `left: ${left}%; bottom: ${bottom}%;`;
 		});
 	}else if(stage1_info.state == "step3-2-selecting"){
-	    selector = "#chkbox-group > div:nth-of-type(2)";
-	    cnt = stage1_info.step3.count;
+		selector = "#chkbox-group > div:nth-of-type(2)";
+		cnt = stage1_info.step3.count;
 	}else return;
 	
 	// update the indication
@@ -234,11 +264,11 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 	stage2_json = gr.JSON({}, visible=False)
 	channel_json = gr.JSON({}, visible=False)
 
-	#gr.Markdown("# ...")
+	gr.HTML(title)
 	with gr.Row():
 
 		with gr.Column(variant="panel"):
-			gr.Markdown("# 1.Channel Mapping")
+			gr.Markdown("## Step1. Channel Mapping")
 			# ---------------------input---------------------
 			in_loc_file = gr.File(label="Channel locations (.loc, .locs, .xyz, .sfp, .txt)",
 							file_types=[".loc", "locs", ".xyz", ".sfp", ".txt"])
@@ -247,13 +277,13 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			desc_md = gr.Markdown(visible=False)
 			out_result_file = gr.File(visible=False)
 			# --------------------mapping--------------------
-			# step1
+			# step1-1
 			with gr.Row():
 				tpl_img = gr.Image("./template_montage.png", label="Template montage", visible=False)
 				mapped_img = gr.Image(label="Matching result", visible=False)
-			# step2
+			# step1-2
 			radio_group = gr.Radio(elem_id="radio-group", visible=False)
-			# step3
+			# step1-3
 			with gr.Row():
 				in_fillmode = gr.Dropdown(choices=["mean", "zero"],
 											value="mean",
@@ -271,7 +301,7 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			# -----------------------------------------------
 		
 		with gr.Column(variant="panel"):
-			gr.Markdown("# 2.Data Denoising")
+			gr.Markdown("## Step2. Data Denoising")
 			# ---------------------input---------------------
 			with gr.Row():
 				in_data_file = gr.File(label="Raw data (.csv)", file_types=[".csv"])
@@ -334,29 +364,29 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			"fileNames" : {
 				"inputData" : in_loc,
 				"originalMontage" : rootpath + stage1_dir + 'input_montage.png',
-		    	"mappedMontage" : rootpath + stage1_dir + 'mapped_montage.png',
-		    	"outputData" : rootpath + stage1_dir + outputname
+				"mappedMontage" : rootpath + stage1_dir + 'mapped_montage.png',
+				"outputData" : rootpath + stage1_dir + outputname
 			},
 			"state" : "step1-initializing",
 			"errorFlag" : False,
-	    	"step2" : {
-	    		"count" : None,
+			"step2" : {
+				"count" : None,
 				"totalNum" : None
-	    	},
-	    	"step3" : {
-	    		"count" : None,
+			},
+			"step3" : {
+				"count" : None,
 				"totalNum" : None
-	    	},
-	    	"unassignedInput" : None,
-	    	"emptyTemplate" : None,
-	    	"batch" : None,
-	    	"mappingResult" : [
-	    	{
+			},
+			"unassignedInput" : None,
+			"emptyTemplate" : None,
+			"batch" : None,
+			"mappingResult" : [
+			{
 				"index" : None,
 				"isOriginalData" : None
 				#"channelUsageNum" : None
 			}
-	    	]
+			]
 		}
 		stage2_info = {}
 		channel_info = {}
@@ -393,8 +423,8 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			stage1_info["errorFlag"] = False
 			return {stage1_json : stage1_info}
 		
-		# ========================================step0=========================================
-		# step0 to step1
+		# =======================================step1-0========================================
+		# step1-0 to step1-1
 		if stage1_info["state"] == "step1-initializing":
 			# match the names
 			stage1_info, channel_info, tpl_montage, in_montage = app_utils.match_name(stage1_info)
@@ -408,13 +438,13 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			unassigned_num = len(stage1_info["unassignedInput"])
 			if unassigned_num == 0:
 				md = """
-				### Step1: Initial Matching and Scaling
+				### Step1-1: Initial Matching and Scaling
 				Below is the result of mapping your channels to our template channels based on their names.
 				"""
 			else:
 				md = """
-				### Step1: Initial Matching and Scaling
-				Below is the result of mapping your channels to our template channels based on their names.  
+				### Step1-1: Initial Matching and Scaling
+				Below is the result of mapping your channels to our template channels based on their names.<br>
 				- channels highlighted in red are those that do not match any template channels.
 				"""
 			stage1_info["state"] = "step1-finished"
@@ -426,17 +456,17 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 					mapped_img : gr.Image(value=filename2, visible=True),
 					next_btn : gr.Button(visible=True)}
 		
-		# ========================================step1=========================================
+		# =======================================step1-1========================================
 		elif stage1_info["state"] == "step1-finished":
 			in_num = len(channel_info["inputNames"])
 			matched_num = 30 - len(stage1_info["emptyTemplate"])
 			
-			# step1 to step4
+			# step1-1 to step1-4
 			if matched_num == 30:
 				md = """
 				### Mapping Result
-				The mapping process has been finished.  
-				Download the file below if you plan to run the models using the <a href="https://github.com/CNElab-Plus/ArtifactRemovalTransformer">source code</a>.
+				The mapping process has been finished.<br>
+				Download the file below if you plan to run the models using the source code.
 				"""
 				# finalize and save the mapping result
 				outputname = stage1_info["fileNames"]["outputData"]
@@ -451,10 +481,10 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 						mapped_img : gr.Image(visible=False),
 						next_btn : gr.Button(visible=False),
 						run_btn : gr.Button(interactive=True)}
-			# step1 to step2
+			# step1-1 to step1-2
 			elif in_num > matched_num: 
 				md = """
-				### Step2: Forwarding Unmatched Channels
+				### Step1-2: Forwarding Unmatched Channels
 				Select one of your unmatched channels to forward its data to the empty template channel 
 				currently indicated in red.
 				"""
@@ -484,10 +514,10 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 							clear_btn : gr.Button(visible=True),
 							step2_btn : gr.Button(visible=True),
 							next_btn : gr.Button(visible=False)}
-			# step1 to step3-1
+			# step1-1 to step1-3-1
 			elif in_num == matched_num:
 				md = """
-				### Step3: Filling Remaining Template Channels
+				### Step1-3: Filling Remaining Template Channels
 				Select one of the methods provided below to fill the remaining template channels.
 				"""
 				stage1_info["state"] = "step3-select-method"
@@ -499,7 +529,7 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 						fillmode_btn : gr.Button(visible=True),
 						next_btn : gr.Button(visible=False)}
 		
-		# ========================================step2=========================================
+		# =======================================step1-2========================================
 		elif stage1_info["state"] == "step2-selecting":
 			
 			if sel_radio != []:
@@ -514,16 +544,16 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 				channel_info["templateDict"][prev_tpl_name]["matched"] = True
 				channel_info["inputDict"][sel_radio]["assigned"] = True
 			
-			# exclude the tpl_channels filled in step2
+			# exclude the tpl_channels filled in step1-2
 			stage1_info["emptyTemplate"] = app_utils.get_empty_template(channel_info["templateNames"],
 																			channel_info["templateDict"])
 			
-			# step2 to step4
+			# step1-2 to step1-4
 			if len(stage1_info["emptyTemplate"]) == 0:
 				md = """
 				### Mapping Result
-				The mapping process has been finished.  
-				Download the file below if you plan to run the models using the <a href="https://github.com/CNElab-Plus/ArtifactRemovalTransformer">source code</a>.
+				The mapping process has been finished.<br>
+				Download the file below if you plan to run the models using the source code.
 				"""
 				outputname = stage1_info["fileNames"]["outputData"]
 				stage1_info, channel_info = app_utils.mapping_result(stage1_info, channel_info, outputname)
@@ -537,10 +567,10 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 						clear_btn : gr.Button(visible=False),
 						next_btn : gr.Button(visible=False),
 						run_btn : gr.Button(interactive=True)}
-			# step2 to step3-1
+			# step1-2 to step1-3-1
 			else:
 				md = """
-				### Step3: Filling Remaining Template Channels
+				### Step1-3: Filling Remaining Template Channels
 				Select one of the methods provided below to fill the remaining template channels.
 				"""
 				stage1_info["state"] = "step3-select-method"
@@ -553,14 +583,14 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 						clear_btn : gr.Button(visible=False),
 						next_btn : gr.Button(visible=False)}
 		
-		# =======================================step3-1========================================
+		# ======================================step1-3-1=======================================
 		elif stage1_info["state"] == "step3-select-method":
-			# step3-1 to step4
+			# step1-3-1 to step1-4
 			if fillmode == "zero":
 				md = """
 				### Mapping Result
-				The mapping process has been finished.  
-				Download the file below if you plan to run the models using the <a href="https://github.com/CNElab-Plus/ArtifactRemovalTransformer">source code</a>.
+				The mapping process has been finished.<br>
+				Download the file below if you plan to run the models using the source code.
 				"""
 				outputname = stage1_info["fileNames"]["outputData"]
 				stage1_info, channel_info = app_utils.mapping_result(stage1_info, channel_info, outputname)
@@ -573,10 +603,10 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 						in_fillmode : gr.Dropdown(visible=False),
 						fillmode_btn : gr.Button(visible=False),
 						run_btn : gr.Button(interactive=True)}
-			# step3-1 to step3-2
+			# step1-3-1 to step1-3-2
 			elif fillmode == "mean":
 				md = """
-				### Step3: Fill the remaining template channels
+				### Step1-3: Fill the remaining template channels
 				The current empty template channel, indicated in red, will be filled with the average 
 				value of the data from the selected channels. (By default, the 4 nearest channels are pre-selected.)
 				"""
@@ -601,23 +631,23 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 				# determine which button to display
 				if stage1_info["step3"]["totalNum"] == 1:
 					return {stage1_json : stage1_info,
-					        desc_md : gr.Markdown(md),
+							desc_md : gr.Markdown(md),
 							in_fillmode : gr.Dropdown(visible=False),
-				    		fillmode_btn : gr.Button(visible=False),
-				    		chkbox_group : gr.CheckboxGroup(choices=channel_info["inputNames"],
+							fillmode_btn : gr.Button(visible=False),
+							chkbox_group : gr.CheckboxGroup(choices=channel_info["inputNames"],
 															value=value, label=label, visible=True),
-				            next_btn : gr.Button(visible=True)}
+							next_btn : gr.Button(visible=True)}
 				else:
 					return {stage1_json : stage1_info,
-					        desc_md : gr.Markdown(md),
+							desc_md : gr.Markdown(md),
 							in_fillmode : gr.Dropdown(visible=False),
-				    		fillmode_btn : gr.Button(visible=False),
-				    		chkbox_group : gr.CheckboxGroup(choices=channel_info["inputNames"],
+							fillmode_btn : gr.Button(visible=False),
+							chkbox_group : gr.CheckboxGroup(choices=channel_info["inputNames"],
 															value=value, label=label, visible=True),
-				            step3_btn : gr.Button(visible=True)}
+							step3_btn : gr.Button(visible=True)}
 		
-		# =======================================step3-2========================================
-		# step3-2 to step4
+		# ======================================step1-3-2=======================================
+		# step1-3-2 to step1-4
 		elif stage1_info["state"] == "step3-2-selecting":
 			
 			prev_tpl_name = stage1_info["emptyTemplate"][stage1_info["step3"]["count"]-1]
@@ -627,8 +657,8 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			
 			md = """
 			### Mapping Result
-			The mapping process has been finished.  
-			Download the file below if you plan to run the models using the <a href="https://github.com/CNElab-Plus/ArtifactRemovalTransformer">source code</a>.
+			The mapping process has been finished.<br>
+			Download the file below if you plan to run the models using the source code.
 			"""
 			outputname = stage1_info["fileNames"]["outputData"]
 			stage1_info, channel_info = app_utils.mapping_result(stage1_info, channel_info, outputname)
@@ -646,11 +676,11 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 		fn = init_next_step,
 		inputs = [stage1_json, channel_json, in_fillmode, radio_group, chkbox_group],
 		outputs = [stage1_json, channel_json, desc_md, out_result_file, tpl_img, mapped_img, radio_group,
-		            in_fillmode, fillmode_btn, chkbox_group, clear_btn, step2_btn, step3_btn, next_btn, run_btn]
+					in_fillmode, fillmode_btn, chkbox_group, clear_btn, step2_btn, step3_btn, next_btn, run_btn]
 	).success(
-	    fn = None,
-	    js = init_js,
-	    inputs = [stage1_json, channel_json],
+		fn = None,
+		js = init_js,
+		inputs = [stage1_json, channel_json],
 		outputs = []
 	)
 	
@@ -658,14 +688,14 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 	# |                                      Stage1-step0                                      |
 	# +========================================================================================+
 	map_btn.click(
-	    fn = reset_all,
-	    inputs = [session_dir, stage1_json, stage2_json, in_loc_file],
-	    outputs = [stage1_json, stage2_json, channel_json, map_btn, desc_md, out_result_file, tpl_img, mapped_img,
-	    			radio_group, in_fillmode, fillmode_btn, chkbox_group, clear_btn, step2_btn, step3_btn, next_btn,
-	    			in_data_file, in_samplerate, run_btn, cancel_btn, batch_md, out_data_file]
+		fn = reset_all,
+		inputs = [session_dir, stage1_json, stage2_json, in_loc_file],
+		outputs = [stage1_json, stage2_json, channel_json, map_btn, desc_md, out_result_file, tpl_img, mapped_img,
+					radio_group, in_fillmode, fillmode_btn, chkbox_group, clear_btn, step2_btn, step3_btn, next_btn,
+					in_data_file, in_samplerate, run_btn, cancel_btn, batch_md, out_data_file]
 	).success(
-	    fn = init_next_step,
-	    inputs = [stage1_json, channel_json, in_fillmode, radio_group, chkbox_group], 
+		fn = init_next_step,
+		inputs = [stage1_json, channel_json, in_fillmode, radio_group, chkbox_group], 
 		outputs = [stage1_json, channel_json, map_btn, desc_md, tpl_img, mapped_img, next_btn]
 	)
 	
@@ -715,22 +745,22 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 			return {stage1_json : stage1_info,
 					channel_json : channel_info,
 					radio_group : gr.Radio(choices=stage1_info["unassignedInput"],
-					                        value=[], label=label),
+											value=[], label=label),
 					step2_btn : gr.Button(visible=False),
 					next_btn : gr.Button(visible=True)}
 		else:
 			return {stage1_json : stage1_info,
 					channel_json : channel_info,
 					radio_group : gr.Radio(choices=stage1_info["unassignedInput"],
-					                        value=[], label=label)}
+											value=[], label=label)}
 	step2_btn.click(
-	    fn = update_radio,
-	    inputs = [stage1_json, channel_json, radio_group],
-	    outputs = [stage1_json, channel_json, radio_group, step2_btn, next_btn]
+		fn = update_radio,
+		inputs = [stage1_json, channel_json, radio_group],
+		outputs = [stage1_json, channel_json, radio_group, step2_btn, next_btn]
 	).success(
-	    fn = None,
-	    js = update_js,
-	    inputs = [stage1_json, channel_json],
+		fn = None,
+		js = update_js,
+		inputs = [stage1_json, channel_json],
 		outputs = []
 	)
 	
@@ -766,25 +796,25 @@ with gr.Blocks(js=js, delete_cache=(3600, 3600)) as demo:
 					chkbox_group : gr.CheckboxGroup(value=value, label=label)}
 	
 	fillmode_btn.click(
-	    fn = init_next_step,
+		fn = init_next_step,
 		inputs = [stage1_json, channel_json, in_fillmode, radio_group, chkbox_group],
 		outputs = [stage1_json, channel_json, desc_md, out_result_file, in_fillmode, fillmode_btn,
-		            chkbox_group, step3_btn, next_btn, run_btn]
+					chkbox_group, step3_btn, next_btn, run_btn]
 	).success(
-	    fn = None,
-	    js = init_js,
-	    inputs = [stage1_json, channel_json],
+		fn = None,
+		js = init_js,
+		inputs = [stage1_json, channel_json],
 		outputs = []
 	)
 	
 	step3_btn.click(
-	    fn = update_chkbox,
-	    inputs = [stage1_json, channel_json, chkbox_group],
-	    outputs = [stage1_json, chkbox_group, step3_btn, next_btn]
+		fn = update_chkbox,
+		inputs = [stage1_json, channel_json, chkbox_group],
+		outputs = [stage1_json, chkbox_group, step3_btn, next_btn]
 	).success(
-	    fn = None,
-	    js = update_js,
-	    inputs = [stage1_json, channel_json],
+		fn = None,
+		js = update_js,
+		inputs = [stage1_json, channel_json],
 		outputs = []
 	)
 	

app_utils.py

@@ -12,13 +12,13 @@ from scipy.optimize import linear_sum_assignment
 from sklearn.neighbors import NearestNeighbors
 
 def get_matched(tpl_names, tpl_dict):
-    return [name for name in tpl_names if tpl_dict[name]["matched"]==True]
+	return [name for name in tpl_names if tpl_dict[name]["matched"]==True]
 
 def get_empty_template(tpl_names, tpl_dict):
-    return [name for name in tpl_names if tpl_dict[name]["matched"]==False]
+	return [name for name in tpl_names if tpl_dict[name]["matched"]==False]
 
 def get_unassigned_input(in_names, in_dict):
-    return [name for name in in_names if in_dict[name]["assigned"]==False]
+	return [name for name in in_names if in_dict[name]["assigned"]==False]
 
 def read_montage(loc_file):
 	tpl_montage = read_custom_montage("./template_chanlocs.loc")
@@ -48,7 +48,7 @@ def read_montage(loc_file):
 	return tpl_montage, in_montage, tpl_dict, in_dict
 
 def match_name(stage1_info):
-    # read the location file
+	# read the location file
 	loc_file = stage1_info["fileNames"]["inputData"]
 	tpl_montage, in_montage, tpl_dict, in_dict = read_montage(loc_file)
 	tpl_names = tpl_montage.ch_names
@@ -78,20 +78,20 @@ def match_name(stage1_info):
 	tpl_names = tpl_montage.ch_names
 	
 	stage1_info.update({
-	    "unassignedInput" : get_unassigned_input(in_names, in_dict),
-	    "emptyTemplate" : get_empty_template(tpl_names, tpl_dict),
-	    "mappingResult" : [
+		"unassignedInput" : get_unassigned_input(in_names, in_dict),
+		"emptyTemplate" : get_empty_template(tpl_names, tpl_dict),
+	 	"mappingResult" : [
 		{
 			"index" : old_idx,
 			"isOriginalData" : is_orig_data
 		}
-	    ]
+		]
 	})
 	channel_info = {
 		"templateNames" : tpl_names,
 		"inputNames" : in_names,
-	    "templateDict" : tpl_dict,
-	    "inputDict" : in_dict
+		"templateDict" : tpl_dict,
+		"inputDict" : in_dict
 	}
 	return stage1_info, channel_info, tpl_montage, in_montage
 
@@ -147,8 +147,8 @@ def align_coords(channel_info, tpl_montage, in_montage):
 		in_dict[name]["coord_3d"] = transformed_in[i].tolist()
 	
 	channel_info.update({
-	    "templateDict" : tpl_dict,
-	    "inputDict" : in_dict
+		"templateDict" : tpl_dict,
+		"inputDict" : in_dict
 	})
 	return channel_info
 
@@ -238,7 +238,7 @@ def find_neighbors(channel_info, empty_tpl_names, old_idx):
 		distances, indices = knn.kneighbors(empty_tpl[i].reshape(1,-1))
 		idx = tpl_dict[name]["index"]
 		old_idx[idx] = indices[0].tolist()
-    
+	
 	return old_idx
 
 def optimal_mapping(channel_info):
@@ -284,7 +284,7 @@ def optimal_mapping(channel_info):
 	# fill the remaining empty tpl_channels
 	empty_tpl_names = get_empty_template(tpl_names, tpl_dict)
 	if empty_tpl_names != []:
-	    old_idx = find_neighbors(channel_info, empty_tpl_names, old_idx)
+		old_idx = find_neighbors(channel_info, empty_tpl_names, old_idx)
 	
 	result = {
 		"index" : old_idx,