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)