added the extra plots

.gitignore

@@ -1,5 +1,6 @@
 # Byte-compiled / optimized / DLL files
 *.zip
+*.png
 Data-*
 drive-*
 __pycache__/

app.py

@@ -19,7 +19,6 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
             repeat_process = gr.Button('Restart process')  
         with gr.Row():
             leg_dropdown = gr.Dropdown(choices=['GZ1', 'GZ2', 'GZ3', 'GZ4'], label='select leg', value='GZ1')
-                    
         with gr.Row():
             plot_box_leg = gr.Plot(label="Filtered Signal Plot")
             plot_box_overlay = gr.Plot(label="Overlay Signal Plot")
@@ -29,16 +28,16 @@ with gr.Blocks(title='Cabasus') as cabasus_sensor:
         
         with gr.Row():
             plot_slice_leg = gr.Plot(label="Sliced Signal Plot")
-            get_real_slice = gr.Plot(label="Real Signal Plot")
+            get_all_slice = gr.Plot(label="Real Signal Plot")
         
         with gr.Row():
             animation = gr.PlayableVideo(label="Animated horse steps")
         
         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_real_slice])
-        leg_dropdown.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider], outputs=[plot_box_leg, plot_slice_leg, get_real_slice])
-        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_real_slice])
-        slice_slider.change(plot_sensor_data_from_json, inputs=[json_file_box, leg_dropdown, slice_slider], outputs=[plot_box_leg, plot_slice_leg, get_real_slice])
+        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])
+        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])
+        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])
+        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])
 
 cabasus_sensor.queue(concurrency_count=2).launch(debug=True)

funcs/plot_func.py

@@ -3,6 +3,7 @@ import matplotlib
 
 import numpy as np
 import pandas as pd
+import seaborn as sns
 import matplotlib.pyplot as plt
 
 matplotlib.use('Agg')
@@ -58,7 +59,8 @@ def plot_sensor_data_from_json(json_file, sensor, slice_select=1):
     plt.legend()
     plt.tight_layout()
 
-    return fig, fig1, None
+
+    return fig, fig1, plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select)
 
 def plot_overlay_data_from_json(json_file, sensors, use_precise_timestamp=False):
     # Read the JSON file
@@ -114,4 +116,48 @@ def plot_slices(original_signal, imputed_signal, precise_slice_points, normal_sl
     plt.ylabel("Signal Amplitude")
     plt.title("Imputed Signal and Slice Points")
 
-    return True
+    return True
+
+def plot_other_sensor_with_same_timestamp(json_file, sensor, slice_select):
+    # 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)
+    
+    # Concatenate the slices and create a new timestamp series with 20ms intervals
+    timestamps = []
+    sensor_data = []
+    slice_item = []
+    
+    for slice_count, slice_dict in enumerate(slices):
+        if slice_count+1 == slice_select:
+
+            start_timestamp = slice_dict["timestamp"]
+
+            for slist in slice_dict.keys():
+                if slist[-1] != sensor[-1]:
+                    continue
+
+                slice_length = len(slice_dict[slist])
+
+                slice_timestamps = [start_timestamp + 20 * i for i in range(slice_length)]
+                timestamps.extend(slice_timestamps)
+                sensor_data.extend(slice_dict[slist])
+
+                slice_item.extend([slist]*len(slice_timestamps))
+    
+    # Create a DataFrame with the sensor data
+    data = pd.DataFrame({'data': sensor_data, 'sensor': slice_item, 'time': timestamps})
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax = sns.lineplot(data, x='time', y='data', hue='sensor')
+    
+    plt.xlabel("Timestamp")
+    plt.ylabel(sensor)
+    plt.legend()
+    plt.tight_layout()
+
+    return fig

funcs/processor.py

@@ -77,10 +77,11 @@ 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, _ = plot_sensor_data_from_json(file, "GZ1")
+    sensor_fig, slice_fig, get_all_slice = plot_sensor_data_from_json(file, "GZ1")
     overlay_fig = plot_overlay_data_from_json(file, ["GZ1", "GZ2", "GZ3", "GZ4"], use_precise_timestamp=True)
 
-    # 
+    # plot all the curves from the timestep
 
 
-    return 'output.csv', file, f'{len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, None
+
+    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

test_plot.py

@@ -1,40 +0,0 @@
-import matplotlib.pyplot as plt
-import json
-import pandas as pd
-import numpy as np
-
-plt.style.use('ggplot')
-
-def plot_overlay_data_from_json(json_file, sensors, use_precise_timestamp=False, slice_select=1):
-    # Read the JSON file
-    with open(json_file, "r") as f:
-        slices = json.load(f)
-
-    # Set up the colormap
-    cmap = plt.get_cmap('viridis')
-
-    # Create subplots for each sensor
-    fig, axs = plt.subplots(len(sensors), 1, figsize=(12, 2 * len(sensors)), sharex=True)
-
-    for idx, sensor in enumerate(sensors):
-        # Plot the overlay of the slices
-        for slice_idx, slice_dict in enumerate(slices):
-            slice_length = len(slice_dict[sensor])
-
-            # Create timestamp array starting from 0 for each slice
-            slice_timestamps = [20 * i for i in range(slice_length)]
-            sensor_data = slice_dict[sensor]
-
-            data = pd.DataFrame({sensor: sensor_data}, index=slice_timestamps)
-            color = cmap(slice_idx / len(slices))
-
-            axs[idx].plot(data[sensor], color=color, label=f'Slice {slice_idx + 1}')
-
-        axs[idx].set_ylabel(sensor)
-
-    axs[-1].set_xlabel("Timestamp")
-    axs[0].legend()
-
-    return fig
-
-plot_overlay_data_from_json('output.json', ["GZ1", "GZ2", "GZ3", "GZ4"], 4)