diff --git a/Signal_prep.py b/Signal_prep.py
index da730fe..0b79d50 100644
--- a/Signal_prep.py
+++ b/Signal_prep.py
@@ -99,11 +99,14 @@ def load_user_emg_data():
 
     return csv_handler.data_container_dict
 
+
 # Takes in a df and outputs np arrays for x and y values
-def prep_df(df:DataFrame):
-    min, duration = Handler.get_min_max_timestamp(df)
-    y = df.iloc[:,1].to_numpy()
-    return y, duration
+def get_xory_from_df(x_or_y, df:DataFrame):
+    swither = {
+        'x': df.iloc[:,0].to_numpy(),
+        'y': df.iloc[:,1].to_numpy()
+    }
+    return swither.get(x_or_y, 0)
 
 # Normalizes a ndarray of a signal to the scale of int16(32767)
 def normalize_wave(y_values):
@@ -112,7 +115,7 @@ def normalize_wave(y_values):
 
 # Takes the FFT of a DataFrame object
 def fft_of_df(df:DataFrame):
-    y_values, duration = prep_df(df)
+    y_values = get_xory_from_df('y', df)
     N = y_values.size
     norm = normalize_wave(y_values)
     N_trans = fftfreq(N, 1 / SAMPLE_RATE)
@@ -121,7 +124,7 @@ def fft_of_df(df:DataFrame):
 
 # Removes noise with db4 wavelet function 
 def wavelet_db4_denoising(df:DataFrame):
-    y_values, duration = prep_df(df)
+    y_values = get_xory_from_df('y', df)
     #y_values = normalize_wave(y_values)
     wavelet = pywt.Wavelet('db4')
     cA, cD = pywt.dwt(y_values, wavelet)
@@ -140,10 +143,17 @@ def soft_threshold_filter(cA, cD):
     cD_filt = cD 
     return cA_filt, cD_filt
 
-# Inverse dwt for brining denoise signal back to the time domain
-def inverse_wavelet(cA_filt, cD_filt):
+# Inverse dwt for brining denoise signal back to the time domainfi
+def inverse_wavelet(df, cA_filt, cD_filt):
     wavelet = pywt.Wavelet('db4')
     y_new_values = pywt.idwt(cA_filt, cD_filt, wavelet)
+    new_len = len(y_new_values)
+    old_len = len(get_xory_from_df('y', df))
+    if new_len > old_len:
+        while new_len > old_len:
+            y_new_values = y_new_values[:-1]
+            new_len = len(y_new_values)
+            old_len = len(get_xory_from_df('y', df))
     return y_new_values
 
 # Plots DataFrame objects
@@ -161,11 +171,16 @@ def plot_arrays(N, y):
 handler = Handler.CSV_handler()
 file = "/Exp20201205_2myo_hardTypePP/HaluskaMarek_20201207_1810/myoLeftEmg.csv"
 df = handler.get_time_emg_table(file, 1)
-N = np.array(range(int(df.iloc[:,1].size + 1)))
-plot_df(df)
+N = get_xory_from_df('x', df)
+#plot_df(df)
+print(len(N))
+print(len(get_xory_from_df('y', df)))
 x, cA, cD = wavelet_db4_denoising(df)
-plot_arrays(x, cA)
+#plot_arrays(x, cA)
+print(len(cA))
 cA_filt, cD_filt = soft_threshold_filter(cA, cD)
-plot_arrays(x, cA_filt)
-y_new_values = inverse_wavelet(cA, cD)
+#plot_arrays(x, cA_filt)
+print(len(cA_filt))
+y_new_values = inverse_wavelet(df, cA, cD)
+print(len(y_new_values))
 plot_arrays(N, y_new_values)
\ No newline at end of file