the slice highlight problem is solved

funcs/plot_func.py

@@ -44,9 +44,7 @@ def plot_sensor_data_from_json(json_file, sensor, slice_select=1):
     ax = plt.plot(data['time'].to_list(), data[sensor].to_list(), '-b')
 
     df_temp = data[data['slice selection'] == int(slice_select)].reset_index()
-    # y = [np.NaN]*((int(slice_select)-1)*len(df_temp[sensor].to_list())) + df_temp[sensor].to_list() + [np.NaN]*((len(slices) - int(slice_select))*len(df_temp[sensor].to_list()))
-    # x = data['time'].to_list()
-    # ax = plt.plot(x, y, '-')
+    ax = plt.plot(df_temp['time'].to_list(), df_temp[sensor].to_list(), '-r')
     
     plt.xlabel("Timestamp")
     plt.ylabel(sensor)

funcs/som.py

@@ -396,21 +396,6 @@ class ClusterSOM:
 
         return video
 
-
-        # # Save the images as a GIF
-        # imageio.mimsave(f"{filename}.gif", images, duration=500, loop=1)
-
-        # # Load the gif
-        # gif_file = f"{filename}.gif"  # Replace with the path to your GIF file
-        # clip = VideoFileClip(gif_file)
-
-        # # Convert the gif to mp4
-        # mp4_file = f"{filename}.mp4"  # Replace with the desired output path
-        # clip.write_videofile(mp4_file, codec='libx264')
-
-        # # Close the clip to release resources
-        # clip.close()
-
     def save(self, file_path):
         """
         Save the ClusterSOM model to a file.

requirements.txt

@@ -1,8 +1,10 @@
+absl-py==1.4.0
 aiofiles==23.1.0
 aiohttp==3.8.4
 aiosignal==1.3.1
 altair==4.2.2
 anyio==3.6.2
+apischema==0.18.0
 appnope==0.1.3
 asttokens==2.2.1
 async-timeout==4.0.2
@@ -86,6 +88,7 @@ pyparsing==3.0.9
 pyrsistent==0.19.3
 python-dateutil==2.8.2
 python-multipart==0.0.6
+pytvlwcharts @ git+https://github.com/TechfaneTechnologies/pytvlwcharts.git@9bc0f70f9bc9569705947e3b7225e109cd01c631
 pytz==2023.3
 PyWavelets==1.4.1
 PyYAML==6.0

test.py

@@ -1,47 +1,56 @@
-import torch
-import gradio as gr
 import json
-import os
-import matplotlib.pyplot as plt
-
-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
-from funcs.dataloader import BaseDataset2, read_json_files
-
-DEVICE = torch.device("cpu")
-reducer10d = PHATEAE(epochs=30, n_components=10, lr=.0001, batch_size=128, t='auto', knn=8, relax=True, metric='euclidean')
-reducer10d.load('models/r10d_2.pth')
-
-cluster_som = ClusterSOM()
-cluster_som.load("models/cluster_som2.pkl")
-
-# ml inference
-def get_som_mp4(file, slice_select, reducer=reducer10d, cluster=cluster_som):
 
-    try:
-        train_x, train_y  = read_json_files(file)
-    except:
-        train_x, train_y  = read_json_files(file.name)
-
-    # Convert tensors to numpy arrays if necessary
-    if isinstance(train_x, torch.Tensor):
-        train_x = train_x.numpy()
-    if isinstance(train_y, torch.Tensor):
-        train_y = train_y.numpy()
-
-    # load the time series slices of the data 4*3*2*64 (feeds+axis*sensor*samples) + 5 for time diff
-    data = BaseDataset2(train_x.reshape(len(train_x), -1) / 32768, train_y)
-
-    #compute the 10 dimensional embeding vector
-    embedding10d = reducer.transform(data)
-
-    # prediction = cluster_som.predict(embedding10d)
-    fig = cluster.plot_activation_v2(embedding10d, slice_select)
-    plt.savefig('test.png')
-    return fig
+import numpy as np 
+import pandas as pd
+import matplotlib.pyplot as plt
 
-get_som_mp4('Data-JSON/Dressage/Tempi/Trab/Arbeitstrab/20210906-093200-Don-Arbeitstrab.json', 1)
+sensor = 'GZ1'
+slice_select = 2
+
+json_file = '/Users/ankit/Documents/cabasus/output.json'
+# 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 = []
+temp_end = 0
+for slice_count, slice_dict in enumerate(slices):
+    start_timestamp = slice_dict["timestamp"]
+    slice_length = len(slice_dict[sensor])
+
+    slice_timestamps = [start_timestamp + 20 * i for i in range(temp_end, slice_length + temp_end)]
+    timestamps.extend(slice_timestamps)
+    sensor_data.extend(slice_dict[sensor])
+
+    temp_end += slice_length
+    slice_item.extend([slice_count+1]*len(slice_timestamps))
+
+# Create a DataFrame with the sensor data
+data = pd.DataFrame({sensor: sensor_data, 'slice selection': slice_item, 'time': timestamps})
+
+# Plot the sensor data
+fig, ax = plt.subplots(figsize=(12, 6))
+ax = plt.plot(data['time'].to_list(), data[sensor].to_list(), '-b')
+
+df_temp = data[data['slice selection'] == int(slice_select)].reset_index()
+ax = plt.plot(df_temp['time'].to_list(), df_temp[sensor].to_list(), '-r')
+
+plt.xlabel("Timestamp")
+plt.ylabel(sensor)
+plt.legend()
+plt.tight_layout()
+
+fig1, ax1 = plt.subplots(figsize=(12, 6))
+ax1 = plt.plot(df_temp['time'].to_list(), df_temp[sensor].to_list())
+
+plt.xlabel("Timestamp")
+plt.ylabel(sensor)
+plt.legend()
+plt.tight_layout()