Added a table tyo summarize output

portiloop/src/demo/demo.py

@@ -33,7 +33,9 @@ def main():
             # Frequency
             freq = gr.Dropdown(choices=["100", "200", "250", "256", "500", "512", "1000", "1024"], value="250", label="Sampling Frequency (Hz)", interactive=True)
 
-        output_array = gr.File(label="Output CSV File")
+        with gr.Row():
+            output_array = gr.File(label="Output CSV File")
+            output_table = gr.Markdown(label="Output Table")
 
         run_inference = gr.Button(value="Run Inference")
         run_inference.click(
@@ -44,7 +46,7 @@ def main():
                 threshold, 
                 detect_channel,
                 freq], 
-            outputs=[output_array])
+            outputs=[output_array, output_table])
 
     demo.queue()
     demo.launch(share=False)

portiloop/src/demo/offline.py

@@ -3,7 +3,7 @@ import numpy as np
 from portiloop.src.detection import SleepSpindleRealTimeDetector
 plt.switch_backend('agg')
 from portiloop.src.processing import FilterPipeline
-from portiloop.src.demo.utils import xdf2array, offline_detect, offline_filter, OfflineSleepSpindleRealTimeStimulator
+from portiloop.src.demo.utils import compute_output_table, xdf2array, offline_detect, offline_filter, OfflineSleepSpindleRealTimeStimulator
 import gradio as gr
 
 
@@ -115,6 +115,12 @@ def run_offline(xdf_file, detect_filter_opts, threshold, channel_num, freq):
     print("Saving output...")
     # Output the data to a csv file
     np.savetxt("output.csv", data_whole, delimiter=",", header=",".join(columns), comments="")
+    
+
+    output_table = compute_output_table(
+        data_whole[:, columns.index("online_stimulations")] if online_detection else data_whole[:, columns.index("online_stimulations_portiloop")],
+        data_whole[:, columns.index("lacourse_spindles")] if lacourse else None, 
+        data_whole[:, columns.index("wamsley_spindles")] if wamsley else None,)
 
     print("Done!")
-    return "output.csv"
+    return "output.csv", output_table

portiloop/src/demo/utils.py

@@ -130,3 +130,22 @@ def offline_filter(signal, freq):
     signal = detrend(signal)
 
     return signal
+
+def compute_output_table(online_stimulation, lacourse_spindles, wamsley_spindles):
+    # Count the number of spindles detected by each method
+    online_stimulation_count = np.sum(online_stimulation)
+    lacourse_spindles_count = sum([1 for index, spindle in enumerate(lacourse_spindles) if spindle == 1 and lacourse_spindles[index - 1] == 0])
+    wamsley_spindles_count = sum([1 for index, spindle in enumerate(wamsley_spindles) if spindle == 1 and wamsley_spindles[index - 1] == 0])
+
+    # Count how many spindles were detected by both online and lacourse
+    both_online_lacourse = sum([1 for index, spindle in enumerate(online_stimulation) if spindle == 1 and lacourse_spindles[index] == 1])
+    # Count how many spindles were detected by both online and wamsley
+    both_online_wamsley = sum([1 for index, spindle in enumerate(online_stimulation) if spindle == 1 and wamsley_spindles[index] == 1])
+
+    # Create markdown table with the results
+    table = "| Method | Detected spindles | Overlap with Portiloop |\n"
+    table += "| --- | --- | --- |\n"
+    table += f"| Online | {online_stimulation_count} | {online_stimulation_count} |\n"
+    table += f"| Lacourse | {lacourse_spindles_count} | {both_online_lacourse} |\n"
+    table += f"| Wamsley | {wamsley_spindles_count} | {both_online_wamsley} |\n"
+    return table