Adding missing modules

wetb/signal/__init__.py

+
+d = None
+d = dir()
+
+from .interpolation import interpolate
+
+
+__all__ = [m for m in set(dir()) - set(d)]
+
+
+
+

wetb/signal/filters/__init__.py

+from ._despike import *
 from ._differentiation import *
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wetb/signal/filters/_differentiation.py

+'''
+Created on 29. mar. 2017
+
+@author: mmpe
+'''
+from wetb.signal.filters.frq_filters import low_pass
+import numpy as np
+def differentiation(x,sample_frq=None, cutoff_frq=None):
+    """Differentiate the signal
+    
+    Parameters
+    ----------
+    x : array_like
+        The input signal
+    sample_frq : int, float or None, optional
+        sample frequency of signal (only required if low pass filer is applied)
+    cutoff_frq : int, float or None, optional
+        Low pass filter cut off (frequencies higher than this frequency will be suppressed)
+    Returns
+    -------
+    y : ndarray
+        differentiated signal
+    
+    
+    Examples
+    --------
+    >>> differentiation([1,2,1,0,1,1])
+    """ 
+ 
+            
+    if cutoff_frq is not None:
+        assert sample_frq is not None, "Argument sample_frq must be set to apply low pass filter"
+        x = low_pass(x, sample_frq, cutoff_frq)
+    else:
+        x = np.array(x) 
+    dy = np.r_[x[1]-x[0], (x[2:]-x[:-2])/2, x[-1]-x[-2]]
+    return dy
\ No newline at end of file

wetb/signal/filters/frq_filters.py

+'''
+Created on 27. mar. 2017
+
+@author: mmpe
+'''
+import numpy as np
+from scipy import signal
+
+def sine_generator(sample_frq, sine_frq, duration):
+    """Create a sine signal for filter test
+    
+    Parameters
+    ----------
+    sample_frq : int, float
+        Sample frequency of returned signal [Hz]
+    sine_frq : int, float
+        Frequency of sine signal [Hz]
+    duration : int, float
+        Duration of returned signal [s]
+        
+    Returns
+    -------
+    t,sig : ndarray, ndarray
+        time (t) and sine signal (sig)
+    
+    Examples
+    --------
+    >>> sine_generator(10,1,2) 
+    """
+    T = duration
+    nsamples = sample_frq * T
+    w = 2. * np.pi * sine_frq
+    t = np.linspace(0, T, nsamples, endpoint=False)
+    sig = np.sin(w * t)
+    return t, sig
+
+
+def low_pass(x, sample_frq, cutoff_frq, order=5):
+    """Low pass filter (butterworth)
+    
+    Parameters
+    ----------
+    x : array_like
+        Input signal
+    sample_frq : int, float
+        Sample frequency [Hz]
+    cutoff_frq : int, float
+        Cut off frequency [Hz]
+    order : int
+        Order of the filter (1th order: 20db per decade, 2th order:)
+    
+    Returns
+    -------
+    y : ndarray
+        Low pass filtered signal
+    
+    
+    Examples
+    --------
+    >>> 
+    """
+    nyquist_frq = 0.5 * sample_frq
+    normal_cutoff = cutoff_frq / nyquist_frq
+    b,a = signal.butter(order, normal_cutoff, btype='low', analog=False)
+    return signal.filtfilt(b, a, x)
+
+def high_pass(x, sample_frq, cutoff_frq, order=5):
+    """Low pass filter (butterworth)
+    
+    Parameters
+    ----------
+    x : array_like
+        Input signal
+    sample_frq : int, float
+        Sample frequency [Hz]
+    cutoff_frq : int, float
+        Cut off frequency [Hz]
+    order : int
+        Order of the filter (1th order: 20db per decade, 2th order:)
+    
+    Returns
+    -------
+    y : ndarray
+        Low pass filtered signal
+    
+    
+    Examples
+    --------
+    >>> 
+    """
+    nyquist_frq = 0.5 * sample_frq
+    normal_cutoff = cutoff_frq / nyquist_frq
+    b,a = signal.butter(order, normal_cutoff, btype='high', analog=False)
+    return signal.filtfilt(b, a, x)
+
+def frequency_response(sample_frq, cutoff_frq, type, order, plt=None):
+    """Frequency response of low/high pass filter (butterworth
+    
+    Parameters
+    ----------
+    sample_frq : int, float
+        Sample frequency [Hz]
+    cutoff_frq : int, float
+        Cut off frequency [Hz]
+    type : {'low','high'}
+        Low or high pass filter
+    order : int
+        Order of the filter (1th order: 20db per decade, 2th order: 40db per decade)
+    plt : pyplot, optional
+        If specified, the frequency response is plotted
+    
+    Returns
+    -------
+    w,h : ndarray, ndarray
+        Frequency (w) in Hz and filter response in db
+    
+    
+    Examples
+    --------
+    >>>  
+    """
+    nyquist_frq = 0.5 * sample_frq
+    normal_cutoff = cutoff_frq / nyquist_frq
+    assert 0<normal_cutoff<1, "cutoff frequency must be <= nyquist frequency"
+    b,a = signal.butter(order, cutoff_frq, btype=type, analog=True)
+    w, h = signal.freqs(b, a)
+    h_db = 20 * np.log10(abs(h))
+    if plt:
+        plt.plot(w, h_db, label='%d order filter response'%order)
+
+        plt.legend(loc=0)
+        
+        title = 'Butterworth filter frequency response'
+        if plt.axes().get_title()!=title:
+            plt.title(title)
+            plt.xlabel('Frequency [Hz]')
+            plt.ylabel('Amplitude [dB]')
+            plt.margins(.1, .1)
+            plt.xscale('log')
+            
+            plt.grid(which='both', axis='both')
+            plt.axvline(cutoff_frq, color='green') # cutoff frequency
+        
+    return w,h_db
\ No newline at end of file

wetb/signal/nan_replace.py

+'''
+Created on 02/11/2015
+
+@author: MMPE
+'''
+
+import numpy as np
+from wetb.signal.filters import replacer
+def replace_by_mean(x):
+    return replacer.replace_by_mean(x, np.isnan(x))
+
+
+def replace_by_line(x):
+    return replacer.replace_by_line(x, np.isnan(x))
+
+def replace_by_polynomial(x, deg=3, no_base_points=12):
+    return replacer.replace_by_polynomial(x, np.isnan(x), deg, no_base_points)
+
+def max_no_nan(x):
+    return replacer.max_cont_mask_length(np.isnan(x))
+
+