[med-svn] [python-mne] 38/52: TEST : adding test for MNE values around 1 nAm

[Yaroslav Halchenko]

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yoh pushed a commit to annotated tag v0.2
in repository python-mne.

commit 60a879783f4bcf8d901c6efcd065e34eb1fcdc43
Author: Alexandre Gramfort <alexandre.gramfort at inria.fr>
Date:   Wed Oct 26 17:06:53 2011 -0400

    TEST : adding test for MNE values around 1 nAm
---
 mne/minimum_norm/inverse.py            |  2 +-
 mne/minimum_norm/tests/test_inverse.py | 14 +++++++++++++-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/mne/minimum_norm/inverse.py b/mne/minimum_norm/inverse.py
index 1064bcf..e99235d 100644
--- a/mne/minimum_norm/inverse.py
+++ b/mne/minimum_norm/inverse.py
@@ -310,7 +310,7 @@ def prepare_inverse_operator(orig, nave, lambda2, dSPM):
         raise ValueError('The number of averages should be positive')
 
     print 'Preparing the inverse operator for use...'
-    inv = orig.copy()
+    inv = deepcopy(orig)
     #
     #   Scale some of the stuff
     #
diff --git a/mne/minimum_norm/tests/test_inverse.py b/mne/minimum_norm/tests/test_inverse.py
index f60dea1..d5fe27d 100644
--- a/mne/minimum_norm/tests/test_inverse.py
+++ b/mne/minimum_norm/tests/test_inverse.py
@@ -48,11 +48,23 @@ def test_inverse_operator():
     With and without precomputed inverse operator.
     """
     evoked = fiff.Evoked(fname_data, setno=0, baseline=(None, 0))
-    stc = apply_inverse(evoked, inverse_operator, lambda2, dSPM)
+    from copy import deepcopy
+
+    stc = apply_inverse(evoked, inverse_operator, lambda2, dSPM=False)
+
+    assert_true(stc.data.min() > 0)
+    assert_true(stc.data.max() < 10e-10)
+    assert_true(stc.data.mean() > 1e-11)
+
+    stc = apply_inverse(evoked, inverse_operator, lambda2, dSPM=True)
 
     assert_true(np.all(stc.data > 0))
     assert_true(np.all(stc.data < 35))
 
+    assert_true(stc.data.min() > 0)
+    assert_true(stc.data.max() < 35)
+    assert_true(stc.data.mean() > 0.1)
+
     # Test MNE inverse computation starting from forward operator
     noise_cov = Covariance(fname_cov)
     evoked = fiff.Evoked(fname_data, setno=0, baseline=(None, 0))

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