fix: change to right naming conventions

Handle_emg_data.py

@@ -487,14 +487,14 @@ class CSV_handler:
         return data_frame, samplerate
 
 
-'''
+    '''
     def get_keyboard_data(self, filename:str, pres_or_release:str='pressed'):
         filepath = self.working_dir + str(filename)
         df = pd.read_csv(filepath)
         if pres_or_release == 'pressed':
             df = df[(df['event'] == 'KeyPressed') and (df['event'] == 'KeyPressed')]
         else
-'''
+    '''
 
 class DL_data_handler:
 
@@ -513,7 +513,7 @@ class DL_data_handler:
                                     4: [],
                                     5: []
                                     }
-                                    
+
     def get_samples_dict(self):
         return self.samples_per_subject
         
@@ -628,48 +628,52 @@ class DL_data_handler:
         with open(json_path, "w") as fp:
             json.dump(data, fp, indent=4)
     '''
-    # HELP FUNCTIONS: ------------------------------------------------------------------------: 
 
-    # Help: gets the str from emg nr
-    def get_emg_str(emg_nr):
-        return 'emg' + str(emg_nr)
+# HELP FUNCTIONS: ------------------------------------------------------------------------: 
 
-    # Help: gets the min/max of a df
-    def get_min_max_timestamp(df:DataFrame):
-        #min = int(np.floor(df['timestamp'].min()))
-        min = df['timestamp'].min()
-        max = df['timestamp'].max()
-        return min, max
+# Help: gets the str from emg nr
+def get_emg_str(emg_nr):
+    return 'emg' + str(emg_nr)
 
-    # Help: returns df_time_emg
-    def make_df_from_xandy(x, y, emg_nr):
-        dict = {'timestamp': x, get_emg_str(emg_nr): y}
-        df = DataFrame(dict)
-        #print(df)
-        return df
+# Help: gets the min/max of a df
+def get_min_max_timestamp(df:DataFrame):
+    #min = int(np.floor(df['timestamp'].min()))
+    min = df['timestamp'].min()
+    max = df['timestamp'].max()
+    return min, max
 
-    # Help: returns the samplerate of a df
-    def get_samplerate(df:DataFrame):
-        min, max = get_min_max_timestamp(df)
-        if max > 60:
-            seconds = max - 60 - min
-        else:
-            seconds = max - min
-        samples = len(df.index)
-        samplerate = samples / seconds
-        return int(samplerate)
+# Help: returns df_time_emg
+def make_df_from_xandy(x, y, emg_nr):
+    dict = {'timestamp': x, get_emg_str(emg_nr): y}
+    df = DataFrame(dict)
+    #print(df)
+    return df
 
-    # Takes in a df and outputs np arrays for x and y values
-    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)
-    
-    # Slightly modified mfcc with inputs like below.
-    # Returns N (x_values from original df) and mfcc_y_values 
-    def mfcc_custom(df:DataFrame, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters):
+# Help: returns the samplerate of a df
+def get_samplerate(df:DataFrame):
+    min, max = get_min_max_timestamp(df)
+    if max > 60:
+        seconds = max - 60 - min
+    else:
+        seconds = max - min
+    samples = len(df.index)
+    samplerate = samples / seconds
+    return int(samplerate)
+
+# Takes in a df and outputs np arrays for x and y values
+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)
+
+# Slightly modified mfcc with inputs like below.
+# Returns N (x_values from original df) and mfcc_y_values 
+def mfcc_custom(df:DataFrame, samplesize, windowsize=MFCC_WINDOWSIZE, 
+                                            stepsize=MFCC_STEPSIZE, 
+                                            nr_coefficients=NR_COEFFICIENTS, 
+                                            nr_mel_filters=NR_MEL_BINS):
         N = get_xory_from_df('x', df)
         y = get_xory_from_df('y', df)
         return N, base.mfcc(y, samplesize, windowsize, stepsize, nr_coefficients, nr_mel_filters)

Present_data.py

@@ -42,7 +42,7 @@ def plot_mfcc(mfcc_data, data_label:str):
     fig, ax = plt.subplots()
     mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
     
-    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
+    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
     ax.xaxis.set_major_formatter(ticks_x)
 
     ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
@@ -55,7 +55,7 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
     
     fig, axes = plt.subplots(nrows=3)
     plt.subplots_adjust(hspace=1.4, wspace=0.4)
-    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
+    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
 
     data_list = [mfcc_data1, mfcc_data2, mfcc_data3]
     label_list = [data_label1, data_label2, data_label3]
@@ -74,7 +74,7 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
 def plot_all_emg_mfcc(data_list:list, label_list:list):
     fig, axes = plt.subplots(nrows=4, ncols=2)
     plt.subplots_adjust(hspace=1.4, wspace=0.4)
-    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * mfcc_stepsize))
+    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * MFCC_STEPSIZE))
     plt.autoscale()
 
     d_list = np.array([ [data_list[0], data_list[4]],
@@ -115,7 +115,7 @@ def pretty(dict):
     
 # 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:CSV_handler, subject_nr, which_arm, round, emg_nr):
     df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
 
     N = get_xory_from_df('x', df)
@@ -123,7 +123,7 @@ 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 = Handler.make_df_from_xandy(N, y_values, emg_nr)
+    df_new = make_df_from_xandy(N, y_values, emg_nr)
     return df_new
 
 
@@ -157,9 +157,9 @@ def mfcc_3_plots_1_1_2(csv_handler:CSV_handler):
     #print(df1.head, samplerate1)
     #print(df2.head, samplerate2)
     #print(df3.head, samplerate3)
-    N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize)
-    N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, mfcc_windowsize, mfcc_stepsize)
-    N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, mfcc_windowsize, mfcc_stepsize)
+    N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
     label_1 = 'Subject 1, session 1, left arm, emg nr. 1'
     label_2 = 'Subject 1, session 2, left arm, emg nr. 1'
     label_3 = 'Subject 2, session 1, left arm, emg nr. 1'
@@ -176,9 +176,9 @@ def mfcc_3_plots_3_3_4(csv_handler:CSV_handler):
     #print(df1.head, samplerate1)
     #print(df2.head, samplerate2)
     #print(df3.head, samplerate3)
-    N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, mfcc_windowsize, mfcc_stepsize)
-    N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, mfcc_windowsize, mfcc_stepsize)
-    N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, mfcc_windowsize, mfcc_stepsize)
+    N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
     label_1 = 'Subject 3, session 1, left arm, emg nr. 1'
     label_2 = 'Subject 3, session 2, left arm, emg nr. 1'
     label_3 = 'Subject 4, session 1, left arm, emg nr. 1'
@@ -194,14 +194,14 @@ def mfcc_all_emg_plots(csv_handler:CSV_handler):
     df6, samplerate6 = csv_handler.get_data( 1, 'left', 1, 6)
     df7, samplerate7 = csv_handler.get_data( 1, 'left', 1, 7)
     df8, samplerate8 = csv_handler.get_data( 1, 'left', 1, 8)
-    N1, mfcc_feat1 = csv_handler.mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N2, mfcc_feat2 = csv_handler.mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N3, mfcc_feat3 = csv_handler.mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N4, mfcc_feat4 = csv_handler.mfcc_custom(df4, samplerate4, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N5, mfcc_feat5 = csv_handler.mfcc_custom(df5, samplerate5, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N6, mfcc_feat6 = csv_handler.mfcc_custom(df6, samplerate6, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N7, mfcc_feat7 = csv_handler.mfcc_custom(df7, samplerate7, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
-    N8, mfcc_feat8 = csv_handler.mfcc_custom(df8, samplerate8, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N1, mfcc_feat1 = mfcc_custom(df1, samplerate1, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N2, mfcc_feat2 = mfcc_custom(df2, samplerate2, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N3, mfcc_feat3 = mfcc_custom(df3, samplerate3, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N4, mfcc_feat4 = mfcc_custom(df4, samplerate4, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N5, mfcc_feat5 = mfcc_custom(df5, samplerate5, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N6, mfcc_feat6 = mfcc_custom(df6, samplerate6, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N7, mfcc_feat7 = mfcc_custom(df7, samplerate7, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
+    N8, mfcc_feat8 = mfcc_custom(df8, samplerate8, MFCC_WINDOWSIZE, MFCC_STEPSIZE)
     feat_list = [mfcc_feat1, mfcc_feat2, mfcc_feat3, mfcc_feat4, mfcc_feat5, mfcc_feat6, mfcc_feat7, mfcc_feat8]
     label_1 = 'Subject 1, session 1, left arm, emg nr. 1'
     label_2 = 'Subject 1, session 1, left arm, emg nr. 2'

Signal_prep.py

@@ -3,7 +3,7 @@ from pandas.core.frame import DataFrame
 from scipy.fft import fft, fftfreq
 import pywt
 import sys
-import Handle_emg_data as Handler
+from Handle_emg_data import *
 
 
 
@@ -17,7 +17,7 @@ def fft_of_df(df:DataFrame):
     y_values = get_xory_from_df('y', df)
     N = y_values.size
     norm = normalize_wave(y_values)
-    N_trans = fftfreq(N, 1 / Handler.get_samplerate(df))
+    N_trans = fftfreq(N, 1 / get_samplerate(df))
     y_f = fft(norm)
     return N_trans, y_f