feat: complete a denoise func for part of the dataset

Handle_emg_data.py

@@ -13,12 +13,18 @@ class Data_container:
         self.data_dict_round2 = {'left': [None]*8, 'right': [None]*8}
         self.data_dict_round3 = {'left': [None]*8, 'right': [None]*8}
         self.data_dict_round4 = {'left': [None]*8, 'right': [None]*8}
+        self.dict_list =    [self.data_dict_round1, 
+                            self.data_dict_round2, 
+                            self.data_dict_round3, 
+                            self.data_dict_round4
+                            ]
     
 class CSV_handler:
 
     def __init__(self):
         self.working_dir = str(Path.cwd()) 
-        self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer 
+        self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer
+        self.data_type = ''
 
     # Makes dataframe from the csv files in the working directory
     def make_df(self, filename):
@@ -158,6 +164,7 @@ class CSV_handler:
                     self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
             # Links the stored data in the data_container to the Handler
             self.link_container_to_handler(data_container)
+        self.data_type = 'hardPP'
         return self.data_container_dict
 
     def load_soft_PP_emg_data(self):
@@ -249,6 +256,7 @@ class CSV_handler:
                     self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
             # Links the stored data in the data_container to the Handler
             self.link_container_to_handler(data_container)
+        self.data_type = 'softPP'
         return self.data_container_dict
 
     def load_hard_original_emg_data(self):
@@ -340,7 +348,7 @@ class CSV_handler:
                     self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
             # Links the stored data in the data_container to the Handler
             self.link_container_to_handler(data_container)
-        
+        self.data_type = 'hard'
         return self.data_container_dict
 
     def load_soft_original_emg_data(self):
@@ -432,6 +440,7 @@ class CSV_handler:
                     self.store_df_in_container(filename, emg_nr, 'right', data_container, round+1)
             # Links the stored data in the data_container to the Handler
             self.link_container_to_handler(data_container)
+        self.data_type = 'soft'
         return self.data_container_dict
 
 # Help: gets the str from emg nr

Signal_prep.py

@@ -1,5 +1,6 @@
 import numpy as np 
 import matplotlib.pyplot as plt
+import pandas
 from pandas.core.frame import DataFrame
 from scipy.fft import fft, fftfreq
 import pywt
@@ -69,6 +70,23 @@ def inverse_wavelet(df, cA_filt, cD_filt):
             old_len = len(get_xory_from_df('y', df))
     return y_new_values
 
+
+def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr, round):
+    data_type = handler.data_type
+    container = handler.data_container_dict.get(subject_nr)
+    df = container.dict_list[round - 1].get(which_arm)[emg_nr]
+    print(df.head)
+
+    N = get_xory_from_df('x', df)
+    N_trans, cA, cD = wavelet_db4_denoising(df)
+    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
+    y_values = inverse_wavelet(df, cA_filt, cD_filt)
+
+    return pandas.DataFrame([N_trans, y_values])
+
+
+
+# MOVE TO Present_data.py
 # Plots DataFrame objects
 def plot_df(df:DataFrame):
     lines = df.plot.line(x='timestamp')