diff options
Diffstat (limited to 'nitime/analysis/tests/test_granger.py')
| -rw-r--r-- | nitime/analysis/tests/test_granger.py | 21 |
1 files changed, 13 insertions, 8 deletions
diff --git a/nitime/analysis/tests/test_granger.py b/nitime/analysis/tests/test_granger.py index 9deb5ca..b9921b6 100644 --- a/nitime/analysis/tests/test_granger.py +++ b/nitime/analysis/tests/test_granger.py @@ -5,11 +5,12 @@ Testing the analysis.granger submodule import numpy as np +import numpy.testing as npt + import nitime.analysis.granger as gc import nitime.utils as utils import nitime.timeseries as ts -import numpy.testing as npt def test_model_fit(): """ @@ -53,22 +54,26 @@ def test_model_fit(): ecov = np.empty((N, n_process, n_process)) for i in xrange(N): - this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1], order=2) + this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], + z[i][1], + order=2) Rxx[i] = this_Rxx coef[i] = this_coef ecov[i] = this_ecov - npt.assert_almost_equal(cov, np.mean(ecov,0), decimal=1) - npt.assert_almost_equal(am, np.mean(coef,0), decimal=1) + npt.assert_almost_equal(cov, np.mean(ecov, axis=0), decimal=1) + npt.assert_almost_equal(am, np.mean(coef, axis=0), decimal=1) # Next we test that the automatic model order estimation procedure works: est_order = [] for i in xrange(N): - this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], z[i][1]) + this_order, this_Rxx, this_coef, this_ecov = gc.fit_model(z[i][0], + z[i][1]) est_order.append(this_order) npt.assert_almost_equal(order, np.mean(est_order), decimal=1) + def test_GrangerAnalyzer(): """ Testing the GrangerAnalyzer class, which simplifies calculations of related @@ -98,11 +103,11 @@ def test_GrangerAnalyzer(): g1 = gc.GrangerAnalyzer(ts1) # Check that things have the right shapes: - npt.assert_equal(g1.frequencies.shape[-1], g1._n_freqs//2 + 1) - npt.assert_equal(g1.causality_xy[0,1].shape, g1.causality_yx[0,1].shape) + npt.assert_equal(g1.frequencies.shape[-1], g1._n_freqs // 2 + 1) + npt.assert_equal(g1.causality_xy[0, 1].shape, g1.causality_yx[0, 1].shape) # Test inputting ij: - g2 = gc.GrangerAnalyzer(ts1, ij = [(0, 1), (1, 0)]) + g2 = gc.GrangerAnalyzer(ts1, ij=[(0, 1), (1, 0)]) # x => y for one is like y => x for the other: npt.assert_almost_equal(g1.causality_yx[1, 0], g2.causality_xy[0, 1]) |