Merge branch 'glm_null_contrasts_rerebased' into main-master

matthew-brett · matthew-brett · commit 002fafc61fc4 · 2013-01-22T12:10:50.000-08:00
* glm_null_contrasts_rerebased:
  Removed useless contrast in test_glm.test_high_level_glm_null_contrasts
  Added test in test_glm.test_high_level_glm_null_contrasts and fixed a glitch in test_glm.generate_fake_fmri_data
  Syntax change in FMRILinearModel.contrast implementation
  FMRILinearModel handles the case where contrasts are null for some fmri sessions
diff --git a/nipy/modalities/fmri/glm.py b/nipy/modalities/fmri/glm.py
@@ -32,6 +32,8 @@
 
 import numpy as np
 
+from warnings import warn
+
 import scipy.stats as sps
 
 from nibabel import load, Nifti1Image
@@ -561,8 +563,11 @@ def contrast(self, contrasts, con_id='', contrast_type=None, output_z=True,
                 'contrasts must be a sequence of %d session contrasts' %
                 len(self.glms))
 
+        contrast_ = None
         for i, (glm, con) in enumerate(zip(self.glms, contrasts)):
-            if i == 0:
+            if np.all(con == 0):
+                warn('Contrast for session %d is null' % i)
+            elif contrast_ is None:
                 contrast_ = glm.contrast(con, contrast_type)
             else:
                 contrast_ = contrast_ + glm.contrast(con, contrast_type)
diff --git a/nipy/modalities/fmri/tests/test_glm.py b/nipy/modalities/fmri/tests/test_glm.py
@@ -34,15 +34,16 @@ def write_fake_fmri_data(shapes, rk=3, affine=np.eye(4)):
 
 
 def generate_fake_fmri_data(shapes, rk=3, affine=np.eye(4)):
-    fmri_data, design_matrices= []
+    fmri_data = []
+    design_matrices = []
     for i, shape in enumerate(shapes):
         data = 100 + np.random.randn(*shape)
         data[0] -= 10
         fmri_data.append(Nifti1Image(data, affine))
         design_matrices.append(np.random.randn(shape[3], rk))
-    mask = Nifti1Image((np.random.rand(*shape[:3]) > .5).astype(np.int8), 
+    mask = Nifti1Image((np.random.rand(*shape[:3]) > .5).astype(np.int8),
                        affine)
-    return mask, fmri_data, design_matrices 
+    return mask, fmri_data, design_matrices
 
 
 def test_high_level_glm_with_paths():
@@ -105,6 +106,21 @@ def test_high_level_glm_contrasts():
         z1.get_data(), z2.get_data())).all())
 
 
+def test_high_level_glm_null_contrasts():
+    shapes, rk = ((5, 6, 7, 20), (5, 6, 7, 19)), 3
+    mask, fmri_data, design_matrices = generate_fake_fmri_data(shapes, rk)
+
+    multi_session_model = FMRILinearModel(
+        fmri_data, design_matrices, mask=None)
+    multi_session_model.fit()
+    single_session_model = FMRILinearModel(
+        fmri_data[:1], design_matrices[:1], mask=None)
+    single_session_model.fit()
+    z1, = multi_session_model.contrast([np.eye(rk)[:1], np.zeros((1, rk))])
+    z2, = single_session_model.contrast([np.eye(rk)[:1]])
+    np.testing.assert_almost_equal(z1.get_data(), z2.get_data())
+
+
 def ols_glm(n=100, p=80, q=10):
     X, Y = np.random.randn(p, q), np.random.randn(p, n)
     glm = GeneralLinearModel(X)
