コード例 #1
0
    def testFIR(self):
        smooth = np.array([0.25, 0.5, 0.25])
    
        d = np.arange(10)
        filtered = fir(d, smooth, zero_edge=False)
        print d, '--', smooth, '(keep edge) -->', filtered
        self.assertTrue(np.all( filtered == d))

        filtered = fir(d, smooth, zero_edge=True)
        print d, '--', smooth, '-->', filtered
        self.assertEquals(0, filtered[9])

        
        d = np.zeros(10)
        d[5] = 10
        filtered = fir(d, smooth)
        print d, '--', smooth, '-->', filtered
        self.assertEquals(5.0, filtered[5])

        d = np.zeros(10)
        d[5:10] = np.ones(5)
        filtered = fir(d, smooth)
        print d, '--', smooth, '-->', filtered
        self.assertEquals(0.0, filtered[3])
        self.assertEquals(0.25, filtered[4])
        self.assertEquals(0.75, filtered[5])
        self.assertEquals(1.0, filtered[6])
        
        diff = np.array([-0.5, 0, 0.5])
        filtered = fir(d, diff)
        print d, '--', diff, '-->', filtered
コード例 #2
0
 def sum(self, axis=None):
     """Sum the matrix over the given axis.  If the axis is None, sum
     over both rows and columns, returning a scalar.
     """
     # We use multiplication by an array of ones to achieve this.
     # For some sparse matrix formats more efficient methods are
     # possible -- these should override this function.
     m, n = self.shape
     if axis == 0:
         # sum over columns
         return np.asmatrix(np.ones((1, m), dtype=self.dtype)) * self
     elif axis == 1:
         # sum over rows
         return self * np.asmatrix(np.ones((n, 1), dtype=self.dtype))
     elif axis is None:
         # sum over rows and columns
         return ( self * np.asmatrix(np.ones((n, 1), dtype=self.dtype)) ).sum()
     else:
         raise ValueError, "axis out of bounds"