def test_pattern_correlation(self):
"""
test pattern correlation function
"""
x = self.D.copy()
# correlation with random values
y = self.D.copy()
tmp = np.random.random(y.shape)
y.data = np.ma.array(tmp, mask=tmp != tmp)
P2 = PatternCorrelation(x, y)
P2._correlate()
self.assertEqual(x.nt,len(P2.r_value))
self.assertEqual(x.nt,len(P2.t))
for i in xrange(x.nt):
slope, intercept, r_value, p_value, std_err = stats.mstats.linregress(x.data[i,:,:].flatten(),y.data[i,:,:].flatten())
self.assertEqual(P2.r_value[i], r_value)
self.assertEqual(P2.p_value[i], p_value)
self.assertEqual(P2.slope[i], slope)
self.assertEqual(P2.intercept[i], intercept)
self.assertEqual(P2.std_err[i], std_err)
def test_pattern_correlation(self):
"""
test pattern correlation function
"""
x = self.D.copy()
# correlation with random values
y = self.D.copy()
tmp = np.random.random(y.shape)
y.data = np.ma.array(tmp, mask=tmp != tmp)
P2 = PatternCorrelation(x, y)
P2._correlate()
self.assertEqual(x.nt, len(P2.r_value))
self.assertEqual(x.nt, len(P2.t))
for i in xrange(x.nt):
slope, intercept, r_value, p_value, std_err = stats.mstats.linregress(
x.data[i, :, :].flatten(), y.data[i, :, :].flatten())
self.assertEqual(P2.r_value[i], r_value)
self.assertEqual(P2.p_value[i], p_value)
self.assertEqual(P2.slope[i], slope)
self.assertEqual(P2.intercept[i], intercept)
self.assertEqual(P2.std_err[i], std_err)
