test: test the load and denoise funcs together

Handle_emg_data.py

@@ -447,7 +447,7 @@ class CSV_handler:
     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]
+        df = container.dict_list[round - 1].get(which_arm)[emg_nr - 1]
         return df
 
 

Signal_prep.py

@@ -52,8 +52,8 @@ def sure_threshold_filter(cA, cD):
 '''
 
 # soft filtering of wavelet trans with the 40% lowest removed
-def soft_threshold_filter(cA, cD):
-    cA_filt = pywt.threshold(cA, 0.4 * cA.max())
+def soft_threshold_filter(cA, cD, threshold):
+    cA_filt = pywt.threshold(cA, threshold * cA.max())
     cD_filt = cD 
     return cA_filt, cD_filt
 
@@ -71,16 +71,16 @@ def inverse_wavelet(df, cA_filt, cD_filt):
     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, round, emg_nr):
+def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, emg_nr, threshold):
     df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
 
     N = get_xory_from_df('x', df)
     N_trans, cA, cD = wavelet_db4_denoising(df)
-    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
+    cA_filt, cD_filt = soft_threshold_filter(cA, cD, threshold)
     y_values = inverse_wavelet(df, cA_filt, cD_filt)
 
     df_new = Handler.make_df_from_xandy(N, y_values, emg_nr)
-    return N, y_values, df_new
+    return df_new
 
 
 

Test_functions.py

@@ -1,3 +1,4 @@
+from matplotlib import lines
 from numpy import load
 from Handle_emg_data import *
 import matplotlib.pyplot as plt
@@ -66,11 +67,19 @@ def test_total_denoising():
     # Original df:
     df = handler.get_df_from_data_dict(3, 'left', 3, 3)
     print(df.head)
-    plot_df(df)
+    
 
     # Denoised df:
-    N_trans, y_values, df_denoised = denoice_dataset(handler, 3, 'left', 3, 3)
+    df_denoised = denoice_dataset(handler, 3, 'left', 3, 3, 0.2)
     print(df_denoised.head)
-    plot_df(df_denoised)
+
+    x = get_xory_from_df('x', df)
+    y1 = get_xory_from_df('y', df)
+    y2 = get_xory_from_df('y', df_denoised)
+
+    figure, axis = plt.subplots(1, 2)
+    axis[0].plot(x, y1)
+    axis[1].plot(x, y2)
+    plt.show()
 
 test_total_denoising()