added revision

app.py

@@ -178,7 +178,7 @@ def scores_to_dataframe(scores, start_time='2022-07-01 09:15:00+05:30', start_sc
     return df
 
 def get_som_mp4_v2(csv_file_box, slice_size_slider, sample_rate, window_size_slider, reducer=reducer10d, cluster=cluster_som):
-    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 = process_data(csv_file_box, 
+    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, time_list = process_data(csv_file_box, 
                                                                                                                                                                   slice_size_slider, 
                                                                                                                                                                   sample_rate, 
                                                                                                                                                                   window_size_slider)
@@ -217,7 +217,6 @@ def get_som_mp4_v2(csv_file_box, slice_size_slider, sample_rate, window_size_sli
                     low=scores_df['low'],
                     close=scores_df['close'])])
 
-
     # Write the processed data to a CSV file
     header = ['Gait', 'TS', 'State', 'Condition', 
               'Shape1', 'Shape2', 'Shape3', 'Shape4', 
@@ -239,8 +238,13 @@ def get_som_mp4_v2(csv_file_box, slice_size_slider, sample_rate, window_size_sli
     #     f.write(response.content)
 
     # prediction = cluster_som.predict(embedding10d)
-    som_video = cluster.plot_activation(embedding10d)
+
+    # passing the time values for each slice
+
+    som_video = cluster.plot_activation(embedding10d, times=time_list)
     som_video.write_videofile('som_sequence.mp4')
+
+    # return 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, som_video, 'animation.mp4', fig
         
     return 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, 'som_sequence.mp4', 'animation.mp4', fig
     return 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, 'som_sequence.mp4', None

funcs/convertors.py

@@ -31,6 +31,7 @@ def slice_csv_to_json(input_file, slice_size=64, min_slice_size=16, sample_rate=
 
     slices = []
     start_index = 0
+    list_time_diff_for_activation = []
     for i, precise_slice_point in enumerate(precise_slice_points):
         end_index = round(precise_slice_point / sample_rate)
         if i == 0:
@@ -69,6 +70,8 @@ def slice_csv_to_json(input_file, slice_size=64, min_slice_size=16, sample_rate=
             slice_data["timestamp"] = timestamp
             slice_data["time_diff"] = time_diff
             slice_data["precise_time_diff"] = precise_time_diff
+            list_time_diff_for_activation.append(slice_data["precise_time_diff"])
+
 
             if end_index - start_index < slice_size:
                 pad_size = slice_size - (end_index - start_index)
@@ -89,7 +92,7 @@ def slice_csv_to_json(input_file, slice_size=64, min_slice_size=16, sample_rate=
     if debug:
         plot_slices(original_data[gz_columns[0]], data[gz_columns[0]], precise_slice_points, precise_slice_points, sample_rate, data.index.values[0])
 
-    return 'output.json', len(slices)
+    return 'output.json', len(slices), list_time_diff_for_activation
 
 
 def slice_csv_to_json_v2(input_file, slice_size=64, min_slice_size=10, sample_rate=20):

funcs/processor.py

@@ -73,10 +73,10 @@ def process_data(input_file, slice_size=64, sample_rate=20, window_size=40, min_
     # Save the resulting DataFrame to a new file
     data.to_csv('output.csv', sep=";", na_rep="NaN", float_format="%.0f")
 
-    file, len_ = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size)
+    file, len_, time_list = slice_csv_to_json('output.csv', slice_size, min_slice_size, sample_rate, window_size=window_size)
     # file, len_ = slice_csv_to_json_v2('output.csv', slice_size, min_slice_size, sample_rate)
     # get the plot automatically
     sensor_fig, slice_fig, get_all_slice, slice_json, overlay_fig = plot_sensor_data_from_json(file, "GZ1") # with the csv file
     # overlay_fig = plot_overlay_data_from_json(file, ["GZ1", "GZ2", "GZ3", "GZ4"])
 
-    return 'output.csv', file, f'num of slices found: {len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, get_all_slice, slice_json
+    return 'output.csv', file, f'num of slices found: {len_}', sensor_fig, overlay_fig, gr.Slider.update(interactive=True, maximum=len_, minimum=1, value=1), slice_fig, get_all_slice, slice_json, time_list

funcs/som.py

@@ -7,7 +7,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 from tqdm import tqdm
-from moviepy.editor import ImageSequenceClip
+from moviepy.editor import ImageSequenceClip, VideoFileClip
 
 class ClusterSOM:
     def __init__(self):
@@ -125,7 +125,7 @@ class ClusterSOM:
 
         return fig, axes
     
-    def plot_activation(self, data, start=None, end=None):
+    def plot_activation(self, data, start=None, end=None, times=None):
         """
         Generate a GIF visualization of the prediction output using the activation maps of individual SOMs.
         """
@@ -189,8 +189,30 @@ class ClusterSOM:
         # if times is None:
         #     times = [500 for _ in range(len(images))]
 
+        # # Make sure `times` has the same length as `images`.
+        # times = times[1:]
+        # times = [int(t) for t in times]
+        # if len(times) != len(images):
+        #     raise ValueError("`times` must have the same length as the number of frames.")
+        
+        # # Save the images as a GIF with custom durations.
+        # imageio.mimsave("som_gif.gif", images, duration=[t / 1000 for t in times], loop=1)
+
+        # # Load the gif
+        # gif_file = "som_gif.gif"
+        # clip = VideoFileClip(gif_file)
+
+        # # Convert the gif to mp4
+        # mp4_file = "som_gif.mp4"
+        # clip.write_videofile(mp4_file, codec='libx264')
+
+        # # Close the clip to release resources
+        # clip.close()
+
+        # return "som_gif.mp4"
+
         # Create the video using moviepy and save it as a mp4 file
-        video = ImageSequenceClip(images, fps=30) 
+        video = ImageSequenceClip(images, fps=2) 
 
         return video