[med-svn] [python-mne] 289/353: FIX: data order, whats_new, test

[Yaroslav Halchenko]

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yoh pushed a commit to tag 0.4
in repository python-mne.

commit 47ab928ead2653d350f200d1bbeec5525d04c948
Author: Martin Luessi <mluessi at nmr.mgh.harvard.edu>
Date:   Tue Jul 17 16:00:26 2012 -0400

    FIX: data order, whats_new, test
---
 doc/source/whats_new.rst            |  1 +
 mne/simulation/source.py            | 39 ++++++++++++++++---------------------
 mne/simulation/tests/test_source.py | 26 +++++++++++++++++++++++--
 3 files changed, 42 insertions(+), 24 deletions(-)

diff --git a/doc/source/whats_new.rst b/doc/source/whats_new.rst
index 1a6d0aa..8749667 100644
--- a/doc/source/whats_new.rst
+++ b/doc/source/whats_new.rst
@@ -6,6 +6,7 @@ Current
 
 Changelog
 ~~~~~~~~~
+   - Generation of simulated evoked responses by `Alex Gramfort`_, `Daniel Strohmeier`_, and `Martin Luessi`_
 
    - Fit AR models to raw data for temporal whitening by `Alex Gramfort`_.
 
diff --git a/mne/simulation/source.py b/mne/simulation/source.py
index bddb5ed..8fec4be 100644
--- a/mne/simulation/source.py
+++ b/mne/simulation/source.py
@@ -45,7 +45,7 @@ def select_source_in_label(src, label, random_state=None):
     return lh_vertno, rh_vertno
 
 
-def generate_sparse_stc(src, labels, stc_data, tmin, tstep, random_state=0):
+def generate_sparse_stc(src, labels, stc_data, tmin, tstep, random_state=None):
     """Generate sparse sources time courses from waveforms and labels
 
     This function randomly selects a single vertex in each label and assigns
@@ -83,14 +83,20 @@ def generate_sparse_stc(src, labels, stc_data, tmin, tstep, random_state=0):
         vertno[0] += lh_vertno
         vertno[1] += rh_vertno
         if len(lh_vertno) != 0:
-            lh_data.append(label_data)
+            lh_data.append(np.atleast_2d(label_data))
         elif len(rh_vertno) != 0:
-            rh_data.append(label_data)
+            rh_data.append(np.atleast_2d(label_data))
         else:
             raise ValueError('No vertno found.')
+
     vertno = map(np.array, vertno)
-    data = np.r_[lh_data, rh_data]
+
+    # the data is in the order left, right
+    lh_data.extend(rh_data)
+    data = np.concatenate(lh_data)
+
     stc = _make_stc(data, tmin, tstep, vertno)
+
     return stc
 
 
@@ -137,8 +143,7 @@ def generate_stc(src, labels, stc_data, tmin, tstep, value_fun=None):
 
     vertno = [[], []]
     stc_data_extended = [[], []]
-    hemi_to_ind = {}
-    hemi_to_ind['lh'],  hemi_to_ind['rh'] = 0, 1
+    hemi_to_ind = {'lh': 0, 'rh': 1}
     for i, label in enumerate(labels):
         hemi_ind = hemi_to_ind[label['hemi']]
         src_sel = np.intersect1d(src[hemi_ind]['vertno'],
@@ -153,7 +158,7 @@ def generate_stc(src, labels, stc_data, tmin, tstep, value_fun=None):
             data = np.tile(stc_data[i], (len(src_sel), 1))
 
         vertno[hemi_ind].append(src_sel)
-        stc_data_extended[hemi_ind].append(data)
+        stc_data_extended[hemi_ind].append(np.atleast_2d(data))
 
     # format the vertno list
     for idx in (0, 1):
@@ -163,21 +168,11 @@ def generate_stc(src, labels, stc_data, tmin, tstep, value_fun=None):
             vertno[idx] = vertno[idx][0]
     vertno = map(np.array, vertno)
 
-    # the data is in the same order as the vertices in vertno
-    n_vert_tot = len(vertno[0]) + len(vertno[1])
-    stc_data = np.zeros((n_vert_tot, stc_data.shape[1]))
-    for idx in (0, 1):
-        if len(stc_data_extended[idx]) == 0:
-            continue
-        if len(stc_data_extended[idx]) == 1:
-            data = stc_data_extended[idx][0]
-        else:
-            data = np.concatenate(stc_data_extended[idx])
-
-        if idx == 0:
-            stc_data[:len(vertno[0]), :] = data
-        else:
-            stc_data[len(vertno[0]):, :] = data
+    # the data is in the order left, right
+    lh_data = stc_data_extended[0]
+    rh_data = stc_data_extended[1]
+    lh_data.extend(rh_data)
+    stc_data = np.concatenate(lh_data)
 
     stc = _make_stc(stc_data, tmin, tstep, vertno)
     return stc
diff --git a/mne/simulation/tests/test_source.py b/mne/simulation/tests/test_source.py
index bae2dd4..f61dfa4 100644
--- a/mne/simulation/tests/test_source.py
+++ b/mne/simulation/tests/test_source.py
@@ -2,14 +2,14 @@ import os.path as op
 import copy
 
 import numpy as np
-from numpy.testing import assert_array_almost_equal
+from numpy.testing import assert_array_almost_equal, assert_array_equal
 from nose.tools import assert_true
 
 from ...datasets import sample
 from ... import read_label
 from ... import read_forward_solution
 
-from ..source import generate_stc
+from ..source import generate_stc, generate_sparse_stc
 
 examples_folder = op.join(op.dirname(__file__), '..', '..', '..' '/examples')
 data_path = sample.data_path(examples_folder)
@@ -46,3 +46,25 @@ def test_generate_stc():
     # the first label has value 0, the second value 2
     assert_array_almost_equal(stc.data[0], np.zeros(n_times))
     assert_array_almost_equal(stc.data[-1], 4 * np.ones(n_times))
+
+
+def test_generate_sparse_stc():
+    """ Test generation of sparse source estimate """
+
+    n_times = 10
+    tmin = 0
+    tstep = 1e-3
+
+    stc_data = np.ones((len(labels), n_times))
+    stc_1 = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, 0)
+
+    assert_true(np.all(stc_1.data == 1.0))
+    assert_true(stc_1.data.shape[0] == len(labels))
+    assert_true(stc_1.data.shape[1] == n_times)
+
+    # make sure we get the same result when using the same seed
+    stc_2 = generate_sparse_stc(fwd['src'], labels, stc_data, tmin, tstep, 0)
+
+    assert_array_equal(stc_1.lh_vertno, stc_2.lh_vertno)
+    assert_array_equal(stc_1.rh_vertno, stc_2.rh_vertno)
+

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