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
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"
