feat: clone and import python_speech_features

and add a mfcc func

.gitignore

@@ -1,4 +1,4 @@
 Exp20201205_2myo_hard**
 Exp20201205_2myo_soft**
 Documents
-python_speech_features
+python_speech_features**

Handle_emg_data.py

@@ -1,7 +1,6 @@
 import pandas as pd
 from pathlib import Path
 import numpy as np
-
 from pandas.core.frame import DataFrame
 
 class Data_container:

Present_data.py

@@ -1,5 +1,7 @@
 from Handle_emg_data import *
 from Signal_prep import *
+import matplotlib.pyplot as plt
+from matplotlib import cm
 
 # PLOT FUNCTIONS:
 
@@ -9,7 +11,7 @@ def plot_df(df:DataFrame):
     plt.show()
 
 # Plots ndarrays after transformations 
-def plot_arrays(N, N_name, y, y_name):
+def plot_array(N, y):
     plt.plot(N, np.abs(y))
     plt.show()
 
@@ -25,6 +27,12 @@ def plot_compare_two_df(df_old, old_name, df_new, new_name):
     axis[1].set_title(new_name)
     plt.show()
 
+def plot_mfcc(mfcc_data):
+    fig, ax = plt.subplots()
+    mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
+    cax = ax.imshow(mfcc_data, interpolation='nearest', cmap=cm.coolwarm, origin='lower')
+    ax.set_title('MFCC')
+    plt.show() 
 
 # DATA FUNCTIONS:
 
@@ -59,6 +67,17 @@ def denoice_dataset(handler:Handler.CSV_handler, subject_nr, which_arm, round, e
     return df_new
 
 
+# CASE FUNTIONS 
+
+def compare_with_wavelet(data_frame):
+    N_trans, cA, cD = wavelet_db4(data_frame)
+    data_frame_freq = make_df_from_xandy(N_trans, cA, 1)
+
+    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
+    data_frame_freq_filt = make_df_from_xandy(N_trans, cD_filt, 1)
+
+    plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
+
 
 # MAIN:
 
@@ -68,13 +87,9 @@ def main():
     load_data(csv_handler, 'hard')
     data_frame = get_data(csv_handler, 1, 'left', 1, 1)
 
-    N_trans, cA, cD = wavelet_db4(data_frame)
+    N, y_mfcc = mfcc(data_frame)
-    data_frame_freq = make_df_from_xandy(N_trans, cA, 1)
+    plt.plot(y_mfcc)
-
+    plt.show()
-    cA_filt, cD_filt = soft_threshold_filter(cA, cD)
-    data_frame_freq_filt = make_df_from_xandy(N_trans, cD_filt, 1)
-
-    plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
 
     return None
 

Signal_prep.py

@@ -1,12 +1,11 @@
 import numpy as np 
-import matplotlib.pyplot as plt
-import pandas
 from pandas.core.frame import DataFrame
 from scipy.fft import fft, fftfreq
 import pywt
-import pyhton_speech_features as psf
+#from pyhton_speech_features.base import mfcc
-#from scipy.signal import wavelets
+import sys
-#import pyyawt
+sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features')
+from python_speech_features.python_speech_features import *
 
 import Handle_emg_data as Handler
 
@@ -78,9 +77,9 @@ def cepstrum(df:DataFrame):
     
     return None
 
-'''
 def mfcc(df:DataFrame):
     N = get_xory_from_df('x', df)
     y = get_xory_from_df('y', df)
-    spf.mfcc(y, )
+    return N, base.mfcc(y, SAMPLE_RATE)
-'''
+    
+