Merge pull request #22 from ins-amu/develop

Develop

Author: Ziaeemehr

.github/workflows/publish.yml

@@ -1772,7 +1772,7 @@ def psd_raw(
         return psd_bands, labels
 
 
-def wavelet_abs_mean_1d(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbose=False):
+def wavelet_abs_mean_1d(ts, function=None, widths=np.arange(1, 10), verbose=False):
     """Computes CWT absolute mean value of each wavelet scale.
 
     Parameters
@@ -1791,10 +1791,13 @@ def wavelet_abs_mean_1d(ts, function=scipy.signal.ricker, widths=np.arange(1, 10
         CWT absolute mean value
 
     """
+    if function is None:
+        function = scipy.signal.ricker
+    
     return tuple(np.abs(np.mean(wavelet(ts, function, widths), axis=1)))
 
 
-def wavelet_abs_mean(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbose=False):
+def wavelet_abs_mean(ts, function=None, widths=np.arange(1, 10), verbose=False):
     '''
     """Computes CWT absolute mean value of each wavelet scale.
 
@@ -1815,6 +1818,9 @@ def wavelet_abs_mean(ts, function=scipy.signal.ricker, widths=np.arange(1, 10),
     labels: array-like
         labels of the features
     '''
+    
+    if function is None:
+        function = scipy.signal.ricker
 
     info, n = prepare_input_ts(ts)
     if not info:
@@ -1835,7 +1841,7 @@ def wavelet_abs_mean(ts, function=scipy.signal.ricker, widths=np.arange(1, 10),
         return values, labels
 
 
-def wavelet_std(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbose=False):
+def wavelet_std(ts, function=None, widths=np.arange(1, 10), verbose=False):
     """
     Computes CWT std value of each wavelet scale.
 
@@ -1857,6 +1863,9 @@ def wavelet_std(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbo
         labels of the features
 
     """
+    
+    if function is None:
+        function = scipy.signal.ricker
 
     info, n = prepare_input_ts(ts)
     if not info:
@@ -1877,7 +1886,7 @@ def wavelet_std(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbo
         return values, labels
 
 
-def wavelet_energy_1d(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbose=False):
+def wavelet_energy_1d(ts, function=None, widths=np.arange(1, 10), verbose=False):
     """Computes CWT energy of each wavelet scale.
 
     Implementation details:
@@ -1901,13 +1910,15 @@ def wavelet_energy_1d(ts, function=scipy.signal.ricker, widths=np.arange(1, 10),
         CWT energy
 
     """
+    if function is None:
+        function = scipy.signal.ricker
     cwt = wavelet(ts, function, widths)
     energy = np.sqrt(np.sum(cwt**2, axis=1) / np.shape(cwt)[1])
 
     return tuple(energy)
 
 
-def wavelet_energy(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), verbose=False):
+def wavelet_energy(ts, function=None, widths=np.arange(1, 10), verbose=False):
     """
     Computes CWT energy of each wavelet scale.
 
@@ -1929,6 +1940,8 @@ def wavelet_energy(ts, function=scipy.signal.ricker, widths=np.arange(1, 10), ve
         labels of the features
 
     """
+    if function is None:
+        function = scipy.signal.ricker
 
     info, n = prepare_input_ts(ts)
     if not info:

vbi/feature_extraction/features.py

@@ -672,38 +672,6 @@ def gaussian(features):
     return np.array(pdf_gauss / np.sum(pdf_gauss))
 
 
-def wavelet(signal, function=scipy.signal.ricker, widths=np.arange(1, 10)):
-    """Computes CWT (continuous wavelet transform) of the signal.
-
-    Parameters
-    ----------
-    signal : nd-array
-        Input from which CWT is computed
-    function :  wavelet function
-        Default: scipy.signal.ricker
-    widths :  nd-array
-        Widths to use for transformation
-        Default: np.arange(1,10)
-
-    Returns
-    -------
-    nd-array
-        The result of the CWT along the time axis
-        matrix with size (len(widths),len(signal))
-
-    """
-
-    if isinstance(function, str):
-        function = eval(function)
-
-    if isinstance(widths, str):
-        widths = eval(widths)
-
-    cwt = scipy.signal.cwt(signal, function, widths)
-
-    return cwt
-
-
 def calc_ecdf(signal):
     """Computes the ECDF of the signal.
      ECDF is the empirical cumulative distribution function.
@@ -1178,7 +1146,7 @@ def one_d(sum1, sum2, sum3):
     return values, labels
 
 
-def wavelet(signal, function=scipy.signal.ricker, widths=np.arange(1, 10)):
+def wavelet(signal, function=None, widths=np.arange(1, 10)):
     """Computes CWT (continuous wavelet transform) of the signal.
 
     Parameters
@@ -1198,6 +1166,9 @@ def wavelet(signal, function=scipy.signal.ricker, widths=np.arange(1, 10)):
         matrix with size (len(widths),len(signal))
 
     """
+    
+    if function is None:
+        function = scipy.signal.ricker
 
     if isinstance(function, str):
         function = eval(function)

vbi/feature_extraction/features_utils.py

@@ -49,4 +49,4 @@ _%.so: %_wrap.o
 clean:
 	rm -rf *.o *.so *.pyc *_wrap.cpp *_wrap.cxx
 	rm -rf __pycache__
-	rm -rf *.py
+	rm -rf do.py jr_sdde.py jr_sde.py km_sde.py mpr_sde.py wc_ode.py vep.py