feat: make df from x and y arrays

Handle_emg_data.py

@@ -461,4 +461,10 @@ def get_min_max_timestamp(df:DataFrame):
     max = df['timestamp'].max()
     return min, max
 
+# Help: returns df_time_emg
+def make_df_from_xandy(x, y, emg_nr):
+    print(type(x))
+    print(type(y))
+    df = DataFrame([x, y], columns=['timestamp', get_emg_str(emg_nr)])
+    return df
 

Signal_prep.py

@@ -79,7 +79,7 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, e
     cA_filt, cD_filt = soft_threshold_filter(cA, cD)
     y_values = inverse_wavelet(df, cA_filt, cD_filt)
 
-    df_new = pandas.DataFrame([N_trans, y_values])
+    df_new = Handler.make_df_from_xandy(N, y_values, emg_nr)
     return N, y_values, df_new
 
 

Test_functions.py

@@ -1,3 +1,4 @@
+from numpy import load
 from Handle_emg_data import *
 import matplotlib.pyplot as plt
 from Signal_prep import *
@@ -58,4 +59,18 @@ def test_soft_load_func():
     print(subject2_container.subject_name)
     print(subject2_container.data_dict_round2.get('right')[3])  # Round2, right, emg_4
 
-test_soft_load_func()
+def test_total_denoising():
+    handler = Handler.CSV_handler('hard')
+    handler.load_hard_original_emg_data()
+
+    # Original df:
+    df = handler.get_df_from_data_dict(3, 'left', 3, 3)
+    print(df.head)
+    plot_df(df)
+
+    # Denoised df:
+    df_denoised = denoice_dataset(handler, 3, 'left', 3, 3)
+    print(df.head)
+    plot_df(df_denoised)
+
+test_total_denoising()