feat: implement func that gets df from

data_dict in handler

Handle_emg_data.py

@@ -21,10 +21,10 @@ class Data_container:
     
 class CSV_handler:
 
-    def __init__(self):
+    def __init__(self, data_type):
         self.working_dir = str(Path.cwd()) 
         self.data_container_dict = {} # Dict with keys equal subject numbers and values equal the relvant datacontainer
-        self.data_type = ''
+        self.data_type = data_type
 
     # Makes dataframe from the csv files in the working directory
     def make_df(self, filename):
@@ -443,6 +443,13 @@ class CSV_handler:
         self.data_type = 'soft'
         return self.data_container_dict
 
+    def get_df_from_data_dict(self, subject_nr, which_arm, round, emg_nr):
+        data_type = self.data_type
+        container = self.data_container_dict.get(subject_nr)
+        df = container.dict_list[round - 1].get(which_arm)[emg_nr]
+        return df
+
+
 # Help: gets the str from emg nr
 def get_emg_str(emg_nr):
     return 'emg' + str(emg_nr)

Signal_prep.py

@@ -70,11 +70,9 @@ def inverse_wavelet(df, cA_filt, cD_filt):
             old_len = len(get_xory_from_df('y', df))
     return y_new_values
 
-
+# Takes in handler and detailes to denoise. Returns arrays and df
 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)
@@ -82,7 +80,8 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, emg_nr,
     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])
+    df_new = pandas.DataFrame([N_trans, y_values])
+    return N, y_values, df_new