added json labelling file

.gitignore

@@ -1,6 +1,7 @@
 # Byte-compiled / optimized / DLL files
 *.mp4
 *.xz
+*.json
 *.gif
 *.zip
 *.png

app.py

@@ -1,8 +1,11 @@
 import torch
 import gradio as gr
+import json
+import os
 
 from phate import PHATEAE
 from funcs.som import ClusterSOM
+from funcs.tools import numpy_to_native
 
 from funcs.processor import process_data
 from funcs.plot_func import plot_sensor_data_from_json
@@ -40,6 +43,23 @@ def get_som_mp4(file, slice_select=1, reducer=reducer10d, cluster=cluster_som):
 
     return fig
 
+def attach_label_to_json(json_file, label_text):
+    # Read the JSON file
+    try:
+        with open(json_file, "r") as f:
+            slices = json.load(f)
+    except:
+        with open(json_file.name, "r") as f:
+            slices = json.load(f)
+    
+    slices['label'] = label_text
+
+    with open(f'manual_labelled_{os.path.basename(json_file.name)}', "w") as f:
+        json.dump(numpy_to_native(slices), f, indent=2)
+    
+    return f'manual_labelled_{os.path.basename(json_file.name)}'
+
+
 with gr.Blocks(title='Cabasus') as cabasus_sensor:
     title = gr.Markdown("<h2><center>Data gathering and processing</center></h2>")
     with gr.Tab("Convert"):
@@ -67,20 +87,27 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
             get_all_slice = gr.Plot(label="Real Signal Plot")
         
         som_create = gr.Button('generate som')
+        som_figures = gr.Plot(label="som activations")
         with gr.Row():
-            som_figures = gr.Plot(label="som activations")
+            slice_json_box = gr.File(label='Slice json file')
+            with gr.Column():
+                label_name = gr.Textbox(label="enter the label name")
+                button_label_Add = gr.Button('attach label')
+            slice_json_label_box = gr.File(label='Slice json labelled file')
+
         
         slices_per_leg = gr.Textbox(label="Number of slices found per LEG")
         
         csv_file_box.change(process_data, inputs=[csv_file_box, slice_size_slider, sample_rate, window_size_slider], 
-                            outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice])
+                            outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice, slice_json_box])
         leg_dropdown.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider], 
-                            outputs=[plot_box_leg, plot_slice_leg, get_all_slice])
+                            outputs=[plot_box_leg, plot_slice_leg, get_all_slice, slice_json_box])
         repeat_process.click(process_data, inputs=[csv_file_box, slice_size_slider, sample_rate, window_size_slider], 
-                             outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice])
+                             outputs=[processed_file_box, json_file_box, slices_per_leg, plot_box_leg, plot_box_overlay, slice_slider, plot_slice_leg, get_all_slice, slice_json_box])
         slice_slider.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider], 
-                            outputs=[plot_box_leg, plot_slice_leg, get_all_slice])
+                            outputs=[plot_box_leg, plot_slice_leg, get_all_slice, slice_json_box])
         
         som_create.click(get_som_mp4, inputs=[json_file_box, slice_slider], outputs=[som_figures])
+        button_label_Add.click(attach_label_to_json, inputs=[slice_json_box, label_name], outputs=[slice_json_label_box])
 
 cabasus_sensor.queue(concurrency_count=2).launch(debug=True)

funcs/plot_func.py

@@ -6,7 +6,7 @@ import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
 
-from funcs.ml_inference import get_som_mp4
+from funcs.tools import numpy_to_native
 
 matplotlib.use('Agg')
 plt.style.use('ggplot')
@@ -61,7 +61,9 @@ def plot_sensor_data_from_json(json_file, sensor, slice_select=1):
     plt.legend()
     plt.tight_layout()
 
-    return fig, fig1, plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select)
+    fig2, file = plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select)
+
+    return fig, fig1, fig2, file
 
 def plot_overlay_data_from_json(json_file, sensors, use_precise_timestamp=False):
     # Read the JSON file
@@ -120,6 +122,9 @@ def plot_slices(original_signal, imputed_signal, precise_slice_points, normal_sl
     return True
 
 def plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select):
+    constant_keys = [f"{sensor}_precise_time_diff", "precise_timestamp",
+                     'timestamp', "time_diff", "precise_time_diff"]
+
     # Read the JSON file
     try:
         with open(json_file, "r") as f:
@@ -132,6 +137,7 @@ def plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select):
     timestamps = []
     sensor_data = []
     slice_item = []
+    slice_recorded = []
     
     for slice_count, slice_dict in enumerate(slices):
         if slice_count+1 == slice_select:
@@ -141,7 +147,7 @@ def plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select):
             for slist in slice_dict.keys():
                 if slist[-1] != sensor[-1]:
                     continue
-
+                slice_recorded.append(slist)
                 slice_length = len(slice_dict[slist])
 
                 slice_timestamps = [start_timestamp + 20 * i for i in range(slice_length)]
@@ -163,4 +169,20 @@ def plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select):
     plt.legend()
     plt.tight_layout()
 
-    return fig
+    #create a new dictionary
+    total_keys = slice_recorded + constant_keys
+    json_dict = {}
+    for tkeys in total_keys:
+        json_dict[tkeys] = slice_dict[tkeys]
+    # json_dict = {'data': sensor_data, 'sensor': slice_item, 'time': timestamps,
+    #              f"{sensor}_precise_time_diff": slice_dict[f"{sensor}_precise_time_diff"],
+    #              "precise_timestamp": slice_dict["precise_timestamp"],
+    #              "timestamp": slice_dict["timestamp"],
+    #              "time_diff": slice_dict["time_diff"],
+    #              "precise_time_diff": slice_dict["precise_time_diff"],
+    #              "label": 'unknown'}
+    
+    with open(f'slice_{slice_select}.json', "w") as f:
+        json.dump(numpy_to_native(json_dict), f, indent=2)
+
+    return fig, f'slice_{slice_select}.json'

funcs/processor.py

@@ -78,7 +78,7 @@ def process_data(input_file, slice_size=64, min_slice_size=16, sample_rate=20, w
     file, len_ = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size)
 
     # get the plot automatically
-    sensor_fig, slice_fig, get_all_slice = plot_sensor_data_from_json(file, "GZ1")
+    sensor_fig, slice_fig, get_all_slice, slice_json = plot_sensor_data_from_json(file, "GZ1")
     overlay_fig = plot_overlay_data_from_json(file, ["GZ1", "GZ2", "GZ3", "GZ4"], use_precise_timestamp=True)
 
-    return 'output.csv', file, f'{len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, get_all_slice
+    return 'output.csv', file, f'{len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, get_all_slice, slice_json