我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用numpy.bartlett()。
def bartlett(M): """ An instance of this class returns the Bartlett spectral window in the time-domain. The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain. .. versionadded:: 0.6 Parameters ---------- M : integer scalar Number of points in the output window. If zero or less, an empty vector is returned. Returns ------- vector of doubles The triangular window, with the maximum value normalized to one (the value one appears only if the number of samples is odd), with the first and last samples equal to zero. """ return bartlett_(M)
def smooth(x,window_len=11,window='hanning'): if x.ndim != 1: raise ValueError, "smooth only accepts 1 dimension arrays." if x.size < window_len: return x # raise ValueError, "Input vector needs to be bigger than window size." if window_len<3: return x if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: raise ValueError, "Window is one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'" s=numpy.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]] if window == 'flat': #moving average w=numpy.ones(window_len,'d') else: w=eval('numpy.'+window+'(window_len)') y=numpy.convolve(w/w.sum(),s,mode='valid') y = y[(window_len/2-1) : -(window_len/2)-1] return y
def perform(self, node, inputs, out_): M = inputs[0] out, = out_ out[0] = numpy.bartlett(M)
def test_perform(self): x = tensor.lscalar() f = function([x], self.op(x)) M = numpy.random.randint(3, 51, size=()) assert numpy.allclose(f(M), numpy.bartlett(M)) assert numpy.allclose(f(0), numpy.bartlett(0)) assert numpy.allclose(f(-1), numpy.bartlett(-1)) b = numpy.array([17], dtype='uint8') assert numpy.allclose(f(b[0]), numpy.bartlett(b[0]))
def voi_noise_window(length): return np.bartlett(length)**2.5 # 2.5 optimum # max: 4 #return np.bartlett(length)**4 #============================================================================== # If win_func == None, no window is applied (i.e., boxcar) # win_func: None, window function, or list of window functions.
def local_bartlett(vec): return(bartlett(len(vec))) # not sure about this, but it is pretty right.
def smooth(x,window_len=11,window='hanning'): """ Smooth the data using a window with requested size. Copied from http://wiki.scipy.org/Cookbook/SignalSmooth This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal. :param x: the input signal :param window_len: the dimension of the smoothing window; should be an odd integer :param window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' flat window will produce a moving average smoothing. :returns: the smoothed signal Example >>> t=linspace(-2,2,0.1) >>> x=sin(t)+randn(len(t))*0.1 >>> y=smooth(x) .. seealso:: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve, scipy.signal.lfilter .. note:: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y. .. todo:: the window parameter could be the window itself if an array instead of a string """ if x.ndim != 1: raise ValueError("smooth only accepts 1 dimension arrays.") if x.size < window_len: raise ValueError("Input vector needs to be bigger than window size.") if window_len<3: return x if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'") s=numpy.r_[x[window_len-1:0:-1],x,x[-1:-window_len:-1]] #print(len(s)) if window == 'flat': #moving average w=numpy.ones(window_len,'d') else: w=eval('numpy.'+window+'(window_len)') y=numpy.convolve(w/w.sum(),s,mode='valid') return y
def smooth(x,window_len=11,window='flat'): """smooth the data using a window with requested size. This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the beginning and end part of the output signal. :param x: the input signal :param window_len: the dimension of the smoothing window; should be an odd integer :param window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' flat window will produce a moving average smoothing. :return: the smoothed signal example:: t=linspace(-2,2,0.1) x=sin(t)+randn(len(t))*0.1 y=smooth(x) :see also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve, scipy.signal.lfilter TODO: the window parameter could be the window itself if an array instead of a string """ if x.ndim != 1: raise ValueError("smooth only accepts 1 dimension arrays.") if x.size < window_len: raise ValueError("Input vector needs to be bigger than window size.") if window_len < 3: return x if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']: raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'") s=numpy.r_[2*x[0]-x[window_len:1:-1],x,2*x[-1]-x[-1:-window_len:-1]] #print(len(s)) if window == 'flat': #moving average w = numpy.ones(window_len,'d') else: w = eval('numpy.' + window + '(window_len)') y = numpy.convolve(w/w.sum(), s, mode='same') return y[window_len-1:-window_len+1]
