adding the corrected animation with proper passing of upsampling window value

app.py

@@ -178,7 +178,10 @@ 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, slice_size_slider, sample_rate, window_size_slider)
+    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, 
+                                                                                                                                                                  slice_size_slider, 
+                                                                                                                                                                  sample_rate, 
+                                                                                                                                                                  window_size_slider)
     print('finished processing')
     try:
         if json_file_box is None:
@@ -225,15 +228,15 @@ def get_som_mp4_v2(csv_file_box, slice_size_slider, sample_rate, window_size_sli
         csv_writer.writerow(header)
         csv_writer.writerows(processed_data)
     
-    # os.system('curl -X POST -F "csv_file=@animation_table.csv" https://metric-space.ngrok.io/generate --output animation.mp4')
-    #with hhtp requests
-    url = "https://metric-space.ngrok.io/generate"
-    file = {'csv_file': open('animation_table.csv', 'rb')}
-    response = requests.post(url, files=file)
-
-    # The response will contain the binary data of the MP4 file. You can write it to a file like this:
-    with open('animation.mp4', 'wb') as f:
-        f.write(response.content)
+    os.system('curl -X POST -F "csv_file=@animation_table.csv" https://metric-space.ngrok.io/generate --output animation.mp4')
+    # #with hhtp requests
+    # url = "https://metric-space.ngrok.io/generate"
+    # file = {'csv_file': open('animation_table.csv', 'rb')}
+    # response = requests.post(url, files=file)
+
+    # # The response will contain the binary data of the MP4 file. You can write it to a file like this:
+    # with open('animation.mp4', 'wb') as f:
+    #     f.write(response.content)
 
     # prediction = cluster_som.predict(embedding10d)
     som_video = cluster.plot_activation(embedding10d)

funcs/convertors.py

@@ -11,9 +11,9 @@ import numpy as np
 import pandas as pd
 
 def slice_csv_to_json(input_file, slice_size=64, min_slice_size=16, sample_rate=20, window_size=20, debug=False):
-    data = pd.read_csv(input_file, delimiter=";", index_col=0)
-    original_data = data.copy()
-    data = fill_missing_values(data, window_size=10)
+    data            = pd.read_csv(input_file, delimiter=";", index_col=0)
+    original_data   = data.copy()
+    data            = fill_missing_values(data, window_size=10)
     data.fillna(0, inplace=True)
 
     gz_columns = [col for col in data.columns if col.startswith("GZ")]

funcs/processor.py

@@ -5,7 +5,7 @@ import gradio as gr
 from funcs.convertors import slice_csv_to_json, slice_csv_to_json_v2
 from funcs.plot_func import plot_sensor_data_from_json
 
-def process_data(input_file, slice_size=64, min_slice_size=10, sample_rate=20, window_size=40, threshold=1000, span_limit=10000000):
+def process_data(input_file, slice_size=64, sample_rate=20, window_size=40, min_slice_size=10, threshold=1000, span_limit=10000000):
     # Read the data from the file, including the CRC column
     try:
         if input_file.name is None:
@@ -16,7 +16,6 @@ def process_data(input_file, slice_size=64, min_slice_size=10, sample_rate=20, w
             return None, None, None, None, None, None, None, None, None
         data = pd.read_csv(input_file, delimiter=";", index_col="NR", usecols=["NR", "TS", "LEG", "GX", "GY", "GZ", "AX", "AY", "AZ", "CRC"])
 
-
     # Replace the values with NaN when the CRC value is not zero
     data.loc[data["CRC"] != 0, ["GX", "GY", "GZ", "AX", "AY", "AZ"]] = np.nan
 
@@ -77,7 +76,7 @@ def process_data(input_file, slice_size=64, min_slice_size=10, sample_rate=20, w
     file, len_ = 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")
+    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