Merge pull request #1 from nilearn/master

Merge origin/master

.travis.yml

@@ -29,7 +29,7 @@ matrix:
            SCIKIT_LEARN_VERSION="0.15"
     # Fake Ubuntu Xenial (Travis doesn't support Xenial yet)
     - env: DISTRIB="conda" PYTHON_VERSION="2.7"
-           NUMPY_VERSION="1.11" SCIPY_VERSION="0.17"
+           NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.17"
            SCIKIT_LEARN_VERSION="0.17"
            NIBABEL_VERSION="2.0.2"
     # Python 3.4 with intermediary versions

doc/index.rst

@@ -21,6 +21,9 @@
 .. |connectome| image:: auto_examples/03_connectivity/images/sphx_glr_plot_inverse_covariance_connectome_004.png
      :target: auto_examples/03_connectivity/plot_inverse_covariance_connectome.html
 
+.. |surface_plot| image:: auto_examples/01_plotting/images/sphx_glr_plot_3d_map_to_surface_projection_001.png
+     :target: auto_examples/01_plotting/plot_3d_map_to_surface_projection.html
+
 .. |haxby_weights| image:: auto_examples/images/sphx_glr_plot_decoding_tutorial_002.png
    :target: auto_examples/plot_decoding_tutorial.html
 
@@ -58,7 +61,7 @@
 
 * |glass_brain|
 
-* |haxby_weights|
+* |surface_plot|
 
 * |oasis_weights|
 
@@ -70,6 +73,8 @@
 
 * |tvl1_haxby|
 
+* |haxby_weights|
+
 * |searchlight|
 
 .. raw:: html

doc/modules/reference.rst

@@ -88,7 +88,7 @@ uses.
    fetch_miyawaki2008
    fetch_nyu_rest
    fetch_surf_nki_enhanced
-   fetch_surf_fsaverage5
+   fetch_surf_fsaverage
    fetch_atlas_surf_destrieux
    fetch_atlas_talairach
    fetch_oasis_vbm

doc/themes/nilearn/layout.html

@@ -195,14 +195,14 @@ <h2>Machine learning for Neuro-Imaging in Python</h2>
 
 <h4> News </h4>
   <ul>
+      <li><p> <strong>June 14th 2018</strong>: Nilearn 0.4.2 released
+      </p></li>
       <li><p> <strong>March 12th 2018</strong>: Nilearn 0.4.1 released
       </p></li>
       <li><p> <strong>November 19th 2017</strong>: Nilearn 0.4 released
       </p></li>
       <li><p> <strong>June 20th 2017</strong>: Nilearn 0.3.1 released
       </p></li>
-      <li><p> <strong>April 18th 2017</strong>: Nilearn 0.3.0 released
-      </p></li>
       <li><p> <strong>March 2014</strong>: <a
           href="http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00014/abstract"
           />Paper</a> describing the concepts at the root of

doc/whats_new.rst

@@ -1,3 +1,51 @@
+0.4.3
+=====
+
+Changes
+-------
+
+    - `nilearn.datasets.fetch_surf_fsaverage5` is deprecated and will be
+      removed in a future release. Use :func:`nilearn.datasets.fetch_surf_fsaverage`,
+      with the parameter mesh="fsaverage5" (the default) instead.
+
+
+Enhancements
+------------
+
+    - Add :func:`nilearn.datasets.fetch_surf_fsaverage` to download either
+      fsaverage or fsaverage 5 (Freesurfer cortical meshes).
+
+
+0.4.2
+=====
+Few important bugs fix release for OHBM conference.
+
+Changes
+-------
+    - Default colormaps for surface plotting functions have changed to be more
+      consistent with slice plotting.
+      :func:`nilearn.plotting.plot_surf_stat_map` now uses "cold_hot", as
+      :func:`nilearn.plotting.plot_stat_map` does, and
+      :func:`nilearn.plotting.plot_surf_roi` now uses "gist_ncar", as
+      :func:`nilearn.plotting.plot_roi` does.
+
+    - Improve 3D surface plotting: lock the aspect ratio of the plots and
+      reduce the whitespace around the plots.
+
+Bug fixes
+---------
+
+    - Fix bug with input repetition time (TR) which had no effect in signal
+      cleaning. Fixed by Pradeep Raamana.
+
+    - Fix issues with signal extraction on list of 3D images in
+      :class:`nilearn.regions.Parcellations`.
+
+    - Fix issues with raising AttributeError rather than HTTPError in datasets
+      fetching utilities. By Jerome Dockes.
+
+    - Fix issues in datasets testing function uncompression of files. By Pierre Glaser.
+
 0.4.1
 =====
 
@@ -328,7 +376,7 @@ Enhancements
     - A function :func:`nilearn.plotting.plot_surf_roi` can be used for
       plotting statistical maps rois onto brain surface.
 
-    - A function :func:`nilearn.datasets.fetch_surf_fsaverage5` can be used
+    - A function `nilearn.datasets.fetch_surf_fsaverage5` can be used
       for surface data object to be as background map for the above plotting
       functions.
 

examples/01_plotting/plot_3d_map_to_surface_projection.py

@@ -23,7 +23,7 @@
 # Get a cortical mesh
 # -------------------
 
-fsaverage = datasets.fetch_surf_fsaverage5()
+fsaverage = datasets.fetch_surf_fsaverage()
 
 ##############################################################################
 # Sample the 3D data around each node of the mesh
@@ -41,8 +41,7 @@
 
 plotting.plot_surf_stat_map(fsaverage.infl_right, texture, hemi='right',
                             title='Surface right hemisphere',
-                            threshold=1., bg_map=fsaverage.sulc_right,
-                            cmap='cold_hot')
+                            threshold=1., bg_map=fsaverage.sulc_right)
 
 ##############################################################################
 # Plot 3D image for comparison
@@ -54,4 +53,22 @@
 plotting.plot_stat_map(localizer_tmap, display_mode='x', threshold=1.,
                        cut_coords=range(0, 51, 10), title='Slices')
 
+
+##############################################################################
+# Plot with higher-resolution mesh
+# --------------------------------
+#
+# `fetch_surf_fsaverage` takes a "mesh" argument which specifies
+# wether to fetch the low-resolution fsaverage5 mesh, or the high-resolution
+# fsaverage mesh. using mesh="fsaverage" will result in more memory usage and
+# computation time, but finer visualizations.
+
+big_fsaverage = datasets.fetch_surf_fsaverage('fsaverage')
+texture = surface.vol_to_surf(localizer_tmap, big_fsaverage.pial_right)
+
+plotting.plot_surf_stat_map(big_fsaverage.infl_right, texture, hemi='right',
+                            title='Surface right hemisphere: fine mesh',
+                            threshold=1., bg_map=big_fsaverage.sulc_right)
+
+
 plotting.show()

examples/01_plotting/plot_surf_atlas.py

@@ -35,7 +35,7 @@
 # Retrieve fsaverage5 surface dataset for the plotting background. It contains
 # the surface template as pial and inflated version and a sulcal depth maps
 # which is used for shading
-fsaverage = datasets.fetch_surf_fsaverage5()
+fsaverage = datasets.fetch_surf_fsaverage()
 
 # The fsaverage dataset contains file names pointing to the file locations
 print('Fsaverage5 pial surface of left hemisphere is at: %s' %
@@ -55,26 +55,26 @@
 plotting.plot_surf_roi(fsaverage['pial_left'], roi_map=parcellation,
                        hemi='left', view='lateral',
                        bg_map=fsaverage['sulc_left'], bg_on_data=True,
-                       darkness=.5, cmap='gist_ncar')
+                       darkness=.5)
 
 ###############################################################################
 # Display Destrieux parcellation on inflated fsaverage5 surface
 plotting.plot_surf_roi(fsaverage['infl_left'], roi_map=parcellation,
                        hemi='left', view='lateral',
                        bg_map=fsaverage['sulc_left'], bg_on_data=True,
-                       darkness=.5, cmap='gist_ncar')
+                       darkness=.5)
 
 ###############################################################################
 # Display Destrieux parcellation with different views: posterior
 plotting.plot_surf_roi(fsaverage['infl_left'], roi_map=parcellation,
                        hemi='left', view='posterior',
                        bg_map=fsaverage['sulc_left'], bg_on_data=True,
-                       darkness=.5, cmap='gist_ncar')
+                       darkness=.5)
 
 ###############################################################################
 # Display Destrieux parcellation with different views: ventral
 plotting.plot_surf_roi(fsaverage['infl_left'], roi_map=parcellation,
                        hemi='left', view='ventral',
                        bg_map=fsaverage['sulc_left'], bg_on_data=True,
-                       darkness=.5, cmap='gist_ncar')
+                       darkness=.5)
 plotting.show()

examples/01_plotting/plot_surf_stat_map.py

@@ -69,7 +69,7 @@
 labels = destrieux_atlas['labels']
 
 # Fsaverage5 surface template
-fsaverage = datasets.fetch_surf_fsaverage5()
+fsaverage = datasets.fetch_surf_fsaverage()
 
 # The fsaverage dataset contains file names pointing to
 # the file locations

examples/02_decoding/plot_haxby_anova_svm.py

@@ -33,9 +33,13 @@
 condition_mask = behavioral['labels'].isin(['face', 'house'])
 conditions = conditions[condition_mask]
 
-# We now have 2 conditions
+# Confirm that we now have 2 conditions
 print(conditions.unique())
-session = behavioral[condition_mask]
+
+# Record these as an array of sessions, with fields
+# for condition (face or house) and run
+session = behavioral[condition_mask].to_records(index=False)
+print(session.dtype.names)
 
 #############################################################################
 # Prepare the fMRI data: smooth and apply the mask
@@ -89,7 +93,7 @@
 # Define the cross-validation scheme used for validation.
 # Here we use a LeaveOneLabelOut cross-validation on the session label
 # which corresponds to a leave-one-session-out
-cv = LeaveOneLabelOut(session['chunks'])
+cv = LeaveOneLabelOut(session)
 
 # Compute the prediction accuracy for the different folds (i.e. session)
 cv_scores = cross_val_score(anova_svc, X, conditions, cv=cv)

nilearn/_utils/exceptions.py

@@ -1,3 +1,10 @@
+try:
+    from numpy import VisibleDeprecationWarning
+except ImportError:
+    class VisibleDeprecationWarning(UserWarning):
+        pass
+
+
 AuthorizedException = (
         BufferError,
         ArithmeticError,

nilearn/datasets/__init__.py

@@ -5,7 +5,8 @@
 from .struct import (fetch_icbm152_2009, load_mni152_template,
                      load_mni152_brain_mask, fetch_oasis_vbm,
                      fetch_icbm152_brain_gm_mask,
-                     MNI152_FILE_PATH, fetch_surf_fsaverage5)
+                     MNI152_FILE_PATH, fetch_surf_fsaverage5,
+                     fetch_surf_fsaverage)
 from .func import (fetch_haxby_simple, fetch_haxby, fetch_nyu_rest,
                    fetch_adhd, fetch_miyawaki2008,
                    fetch_localizer_contrasts, fetch_abide_pcp,
@@ -41,6 +42,7 @@
            'fetch_atlas_yeo_2011', 'fetch_mixed_gambles', 'fetch_atlas_aal',
            'fetch_megatrawls_netmats', 'fetch_cobre',
            'fetch_surf_nki_enhanced', 'fetch_surf_fsaverage5',
+           'fetch_surf_fsaverage',
            'fetch_atlas_basc_multiscale_2015', 'fetch_coords_dosenbach_2010',
            'fetch_neurovault', 'fetch_neurovault_ids',
            'load_mni152_brain_mask', 'fetch_icbm152_brain_gm_mask',

nilearn/datasets/description/fsaverage.rst

@@ -0,0 +1,21 @@
+fsaverage
+
+
+Notes
+-----
+Fsaverage standard surface as distributed with Freesurfer (Fischl et al, 1999)
+
+Content
+-------
+    :'pial_left': Gifti file, left hemisphere pial surface mesh
+    :'pial_right': Gifti file, right hemisphere pial surface mesh
+    :'infl_left': Gifti file, left hemisphere inflated pial surface mesh
+    :'infl_right': Gifti file, right hemisphere inflated pial
+                   surface mesh
+    :'sulc_left': Gifti file, left hemisphere sulcal depth data
+    :'sulc_right': Gifti file, right hemisphere sulcal depth data
+
+References
+----------
+Fischl et al, (1999). High-resolution intersubject averaging and a
+coordinate system for the cortical surface. Hum Brain Mapp 8, 272-284.

nilearn/datasets/func.py

@@ -17,6 +17,7 @@
 from .._utils import check_niimg
 from .._utils.compat import BytesIO, _basestring, _urllib
 from .._utils.numpy_conversions import csv_to_array
+from .._utils.exceptions import VisibleDeprecationWarning
 
 
 @deprecated("fetch_haxby_simple will be removed in future releases. "
@@ -148,9 +149,10 @@ def fetch_haxby(data_dir=None, n_subjects=None, subjects=(2,),
     The anatomical image for subject 6 is unavailable.
     """
     if n_subjects is not None:
-        warnings.warn("The parameter 'n_subjects' is deprecated from 0.2.6 "
-                      "and will be removed in nilearn next release. Use "
-                      "parameter 'subjects' instead.")
+        warn_str = ("The parameter 'n_subjects' is deprecated from 0.2.6 and "
+                    "will be removed in nilearn next release. Use parameter "
+                    "'subjects' instead.")
+        warnings.warn(warn_str, VisibleDeprecationWarning, stacklevel=2)
         subjects = n_subjects
 
     if isinstance(subjects, numbers.Number) and subjects > 6: