feat: add class as a data_container for each subject

Handle_emg_data.py

@@ -1,10 +1,20 @@
 import pandas as pd
 from pathlib import Path
 
+class Data_container:
+      
+    def __init__(self):
+        self.subject_nr
+        self.subject_name
+        self.data_dict
+    
+    
+
 class CSV_handler:
 
     def __init__(self):
         self.working_dir = str(Path.cwd())
+        self.data_container_dict = {i: []}
 
     # Makes dataframe from the csv files in the working directory
     def make_df(self, filename):
@@ -13,15 +23,19 @@ class CSV_handler:
         return df
 
     # Extracts out the timestamp and the selected emg signal into a new dataframe 
-    def get_time_emg_table(self, filename, emg_nr):
+    def get_time_emg_table(self, filename: str, subject_nr: int, which_arm: str, emg_nr: int):
         tot_data_frame = self.make_df(filename)
         emg_str = 'emg' + str(emg_nr)
         filtered_df = tot_data_frame[["timestamp", emg_str]]
+
+        #self.data_dict[subject_nr] = [which_arm, emg1]
+
         return filtered_df
     
     def get_emg_str(emg_nr):
         return 'emg' + str(emg_nr)
 
+    def get_min_max_timestamp():
 
 
 

Signal_prep.py

@@ -1,5 +1,6 @@
 import numpy as np 
-
+from Handle_emg_data import CSV_handler
+import matplotlib.pyplot as plt