fix: add minor changes

.gitignore

@@ -1,3 +1,4 @@
 Exp20201205_2myo_hard**
 Exp20201205_2myo_soft**
 Documents
+python_speech_features

Present_data.py

@@ -9,20 +9,20 @@ def plot_df(df:DataFrame):
     plt.show()
 
 # Plots ndarrays after transformations 
-def plot_arrays(N, y):
+def plot_arrays(N, N_name, y, y_name):
     plt.plot(N, np.abs(y))
     plt.show()
 
-def plot_compare_two_df(df_old, df_new):
+def plot_compare_two_df(df_old, old_name, df_new, new_name):
     x = get_xory_from_df('x', df_old)
     y1 = get_xory_from_df('y', df_old)
     y2 = get_xory_from_df('y', df_new)
 
     figure, axis = plt.subplots(1, 2)
     axis[0].plot(x, y1)
-    axis[0].set_title('Original data')
+    axis[0].set_title(old_name)
     axis[1].plot(x, y2)
-    axis[1].set_title('Analyzed data')
+    axis[1].set_title(new_name)
     plt.show()
 
 
@@ -46,6 +46,19 @@ def get_data(csv_handler:CSV_handler, subject_nr, which_arm, session, emg_nr):
     data_frame = csv_handler.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
     return data_frame
     
+#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):
+    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(df)
+    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)
+    return df_new
+
+
 
 # MAIN:
 
@@ -55,13 +68,13 @@ def main():
     load_data(csv_handler, 'hard')
     data_frame = get_data(csv_handler, 1, 'left', 1, 1)
 
-    N_trans, cA, cD = wavelet_db4_denoising(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, 0.6)
-    data_frame_freq_filt = make_df_from_xandy(N_trans, cA_filt, 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, data_frame_freq_filt)
+    plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
 
     return None
 

Signal_prep.py

@@ -4,6 +4,7 @@ import pandas
 from pandas.core.frame import DataFrame
 from scipy.fft import fft, fftfreq
 import pywt
+import pyhton_speech_features as psf
 #from scipy.signal import wavelets
 #import pyyawt
 
@@ -35,7 +36,7 @@ def fft_of_df(df:DataFrame):
     return N_trans, y_f
 
 # Removes noise with db4 wavelet function 
-def wavelet_db4_denoising(df:DataFrame):
+def wavelet_db4(df:DataFrame):
     y_values = get_xory_from_df('y', df)
     #y_values = normalize_wave(y_values)
     wavelet = pywt.Wavelet('db4')
@@ -51,10 +52,10 @@ def sure_threshold_filter(cA, cD):
     return cA_filt, cD_filt
 '''
 
-# soft filtering of wavelet trans with the 40% lowest removed
-def soft_threshold_filter(cA, cD, threshold):
-    cA_filt = pywt.threshold(cA, threshold * cA.max())
-    cD_filt = cD 
+# soft filtering of wavelet trans with the a 1/2 std filter 
+def soft_threshold_filter(cA, cD):
+    cA_filt = pywt.threshold(cA, np.std(cA)/2)
+    cD_filt = pywt.threshold(cD, np.std(cD)/2)
     return cA_filt, cD_filt
 
 # Inverse dwt for brining denoise signal back to the time domainfi
@@ -70,16 +71,16 @@ def inverse_wavelet(df, cA_filt, cD_filt):
             old_len = len(get_xory_from_df('y', df))
     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, threshold):
-    df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
-
+def cepstrum(df:DataFrame):
     N = get_xory_from_df('x', df)
-    N_trans, cA, cD = wavelet_db4_denoising(df)
-    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 df_new
-
+    y = get_xory_from_df('y', df)
+    
+    
+    return None
 
+'''
+def mfcc(df:DataFrame):
+    N = get_xory_from_df('x', df)
+    y = get_xory_from_df('y', df)
+    spf.mfcc(y, )
+'''

python_speech_features.egg-info/PKG-INFO

@@ -0,0 +1,10 @@
+Metadata-Version: 1.0
+Name: python-speech-features
+Version: 0.6.1
+Summary: Python Speech Feature extraction
+Home-page: https://github.com/jameslyons/python_speech_features
+Author: James Lyons
+Author-email: james.lyons0@gmail.com
+License: MIT
+Description: UNKNOWN
+Platform: UNKNOWN

python_speech_features.egg-info/SOURCES.txt

@@ -0,0 +1,7 @@
+python_speech_features/example.py
+python_speech_features/setup.py
+python_speech_features.egg-info/PKG-INFO
+python_speech_features.egg-info/SOURCES.txt
+python_speech_features.egg-info/dependency_links.txt
+python_speech_features.egg-info/requires.txt
+python_speech_features.egg-info/top_level.txt

python_speech_features.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

python_speech_features.egg-info/requires.txt

@@ -0,0 +1,2 @@
+numpy
+scipy

python_speech_features.egg-info/top_level.txt

@@ -0,0 +1 @@
+python_speech_features