[WIP] test examples to replace ADHD with MAIN datasets

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,40 @@
+---
+name: Bug report
+about: Something not working as described? Missing/incorrect documentation? This is the place.
+title: ''
+labels: 'bug'
+assignees: ''
+
+---
+<!--
+
+Hi! 
+If you have: 
+ -1 Questions about how to use Nilearn or 
+ -2 Need analysis suggestions & recommendations?
+
+A bunch of fMRI researchers hang out at Neurostars (http://neurostars.org/). 
+Post those questions there. 
+Add the tag `nilearn` (we get an email from Neurostars if you do).
+
+Posting them here makes life more complicated for the Nilearn developers.  
+-->
+
+<!--
+
+For the Bug Report, 
+Include this information:
+-------------------------
+What version of Nilearn are you using?
+What were you trying to do?
+What did you expect will happen?
+What actually happened?
+
+List the steps you performed that revealed the bug to you.
+Include any code samples. Enclose them in triple back-ticks (```)
+Like this:
+
+```
+<code>
+``` 
+-->

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,30 @@
+---
+name: Feature request
+about: Got an idea for a new feature, or changing an existing one? This is the place.
+title: ''
+labels: 'feature'
+assignees: ''
+
+---
+<!--
+Hi! 
+If you have: 
+ -1 Questions about how to use Nilearn or 
+ -2 Need analysis suggestions & recommendations?
+
+A bunch of fMRI researchers hang out at Neurostars (http://neurostars.org/). 
+Post those questions there. 
+Add the tag `nilearn` (we get an email from Neurostars if you do).
+
+Posting them here makes life more complicated for the Nilearn developers.  
+-->
+
+<!-- 
+For the Feature Request,
+Include the following:
+------------------------
+What would you like changed/added and why?
+What would be the benefit? Does the change make something easier to use?
+Clarifies something?
+If it is a new feature, what is the benefit? 
+-->

doc/manipulating_images/masker_objects.rst

@@ -101,7 +101,7 @@ slice and create a :ref:`Niimg <niimg>` in memory:
 
 
 .. literalinclude:: ../../examples/04_manipulating_images/plot_mask_computation.py
-    :start-after: Load ADHD resting-state dataset
+    :start-after: Load MAIN resting-state dataset
     :end-before: # To display the background
 
 Controlling how the mask is computed from the data

doc/modules/reference.rst

@@ -87,6 +87,7 @@ uses.
    fetch_localizer_contrasts
    fetch_localizer_calculation_task
    fetch_miyawaki2008
+   fetch_main
    fetch_nyu_rest
    fetch_surf_nki_enhanced
    fetch_surf_fsaverage

doc/plotting/index.rst

@@ -166,6 +166,10 @@ Different display modes
      :target: ../auto_examples/01_plotting/plot_demo_more_plotting.html
      :scale: 50
 
+.. |plot_tiled| image:: ../auto_examples/01_plotting/images/sphx_glr_plot_demo_more_plotting_009.png
+     :target: ../auto_examples/01_plotting/plot_demo_more_plotting.html
+     :scale: 50
+
 .. |plot_lzr| image:: ../auto_examples/01_plotting/images/sphx_glr_plot_demo_glass_brain_extensive_006.png
      :target: ../auto_examples/01_plotting/plot_demo_glass_brain_extensive.html
      :scale: 50
@@ -216,6 +220,11 @@ Different display modes
                    Cutting in the y and z direction, with cuts manually
                    positionned
 
+|plot_tiled|       `display_mode='tiled', cut_coords=[36, -27, 60]`
+                   |hack|
+                   Tiled slicer: 3 cuts along the x, y, z directions,
+                   arranged in a 2x2 grid
+
 |plot_lzr|         `Glass brain display_mode='lzr'`
                    |hack|
                    Glass brain and Connectome provide additional display modes

doc/whats_new.rst

@@ -1,3 +1,37 @@
+0.5.1
+=====
+
+NEW
+---
+
+- Calculate image data dtype from header information
+- New display mode 'tiled' which allows 2x2 plot arrangement when plotting three cuts
+  (see :ref:`plotting`).
+- NiftiLabelsMasker now consumes less memory when extracting the signal from a 3D/4D
+  image. This is especially noteworthy when extracting signals from large 4D images.
+
+Changes
+-------
+
+- Lighting used for interactive surface plots changed; plots may look a bit
+  different.
+- plotting.view_connectome default colormap is `bwr`, consistent with plot_connectome.
+- plotting.view_connectome parameter names are consistent with plot_connectome:
+
+ - coords is now node_coord
+ - marker_size is noe node_size
+ - cmap is now edge_cmap
+ - threshold is now edge_threshold
+
+- plotting.view_markers and plotting.view_connectome can accept different marker
+  sizes for each node / marker.
+
+- plotting.view_markers() default marker color is now 'red', consistent with add_markers().
+- plotting.view_markers() parameter names are consistent with add_markers():
+
+ - coords is now marker_coords
+ - colors is now marker_color
+
 0.5.0
 =====
 

examples/01_plotting/plot_demo_more_plotting.py

@@ -15,7 +15,7 @@
 
 The parameter `display_mode` is used to draw brain slices along given
 specific directions, where directions can be one of 'ortho',
-'x', 'y', 'z', 'xy', 'xz', 'yz'. whereas parameter `cut_coords`
+'tiled','x', 'y', 'z', 'yx', 'xz', 'yz'. whereas parameter `cut_coords`
 is used to specify a limited number of slices to visualize along given
 specific slice direction. The parameter `cut_coords` can also be used
 to draw the specific cuts in the slices by giving its particular
@@ -111,7 +111,7 @@
 ########################################
 # Changing the views to 'coronal', 'sagittal' views with coordinates
 # -------------------------------------------------------------------
-# display_mode='yx' for coronal and saggital view and coordinates will be
+# display_mode='yx' for coronal and sagittal view and coordinates will be
 # assigned in the order of direction as [x, y, z]
 plotting.plot_stat_map(stat_img, display_mode='yx',
                        cut_coords=[-27, 36],
@@ -125,6 +125,15 @@
                        cut_coords=[-27, 60],
                        title="display_mode='yz', cut_coords=[-27, 60]")
 
+########################################
+# Visualizing three views in 2x2 fashion
+# -------------------------------------------------------------------------
+# display_mode='tiled' for sagittal, coronal and axial view
+
+plotting.plot_stat_map(stat_img, display_mode='tiled',
+                       cut_coords=[36, -27, 60],
+                       title="display_mode='tiled'")
+
 ###############################################################################
 # Demonstrating various display features
 # ---------------------------------------

examples/03_connectivity/plot_adhd_spheres.py

@@ -13,11 +13,11 @@
 # Retrieve the dataset
 # ---------------------
 from nilearn import datasets
-adhd_dataset = datasets.fetch_adhd(n_subjects=1)
+main_dataset = datasets.fetch_main(n_subjects=1)
 
 # print basic information on the dataset
 print('First subject functional nifti image (4D) is at: %s' %
-      adhd_dataset.func[0])  # 4D data
+      main_dataset.func[0])  # 4D data
 
 
 ##########################################################################
@@ -43,8 +43,8 @@
     low_pass=0.1, high_pass=0.01, t_r=2.5,
     memory='nilearn_cache', memory_level=1, verbose=2)
 
-func_filename = adhd_dataset.func[0]
-confound_filename = adhd_dataset.confounds[0]
+func_filename = main_dataset.func[0]
+confound_filename = main_dataset.confounds[0]
 
 time_series = masker.fit_transform(func_filename,
                                    confounds=[confound_filename])

examples/03_connectivity/plot_atlas_comparison.py

@@ -35,7 +35,7 @@
 #########################################################################
 # Load functional data
 # --------------------
-data = datasets.fetch_adhd(n_subjects=10)
+data = datasets.fetch_main(n_subjects=10)
 
 print('Functional nifti images (4D, e.g., one subject) are located at : %r'
       % data['func'][0])

examples/03_connectivity/plot_canica_resting_state.py

@@ -35,8 +35,8 @@
 # -------------------------------
 from nilearn import datasets
 
-adhd_dataset = datasets.fetch_adhd(n_subjects=30)
-func_filenames = adhd_dataset.func  # list of 4D nifti files for each subject
+main_dataset = datasets.fetch_main(n_subjects=30)
+func_filenames = main_dataset.func  # list of 4D nifti files for each subject
 
 # print basic information on the dataset
 print('First functional nifti image (4D) is at: %s' %

examples/03_connectivity/plot_compare_resting_state_decomposition.py

@@ -26,12 +26,12 @@
 # -----------------------
 from nilearn import datasets
 
-adhd_dataset = datasets.fetch_adhd(n_subjects=30)
-func_filenames = adhd_dataset.func  # list of 4D nifti files for each subject
+main_dataset = datasets.fetch_main(n_subjects=30)
+func_filenames = main_dataset.func  # list of 4D nifti files for each subject
 
 # print basic information on the dataset
 print('First functional nifti image (4D) is at: %s' %
-      adhd_dataset.func[0])  # 4D data
+      main_dataset.func[0])  # 4D data
 
 ###############################################################################
 # Create two decomposition estimators
@@ -43,7 +43,7 @@
 ###############################################################################
 # Dictionary learning
 # --------------------
-# 
+#
 # We use as "template" as a strategy to compute the mask, as this leads
 # to slightly faster and more reproducible results. However, the images
 # need to be in MNI template space
@@ -90,7 +90,7 @@
 from nilearn.image import index_img
 
 # Selecting specific maps to display: maps were manually chosen to be similar
-indices = {dict_learning: 25, canica: 33}
+indices = {dict_learning: 24, canica: 32}
 # We select relevant cut coordinates for displaying
 cut_component = index_img(components_imgs[0], indices[dict_learning])
 cut_coords = find_xyz_cut_coords(cut_component)

examples/03_connectivity/plot_extract_regions_dictlearning_maps.py

@@ -6,7 +6,7 @@
 to extract spatially constrained brain regions from whole brain maps decomposed
 using dictionary learning and use them to build a functional connectome.
 
-We used 20 resting state ADHD functional datasets from :func:`nilearn.datasets.fetch_adhd`
+We used 20 resting state MAIN functional datasets from :func:`nilearn.datasets.fetch_main`
 and :class:`nilearn.decomposition.DictLearning` for set of brain atlas maps.
 
 This example can also be inspired to apply the same steps to even regions extraction
@@ -32,9 +32,9 @@
 # We use nilearn's datasets downloading utilities
 from nilearn import datasets
 
-adhd_dataset = datasets.fetch_adhd(n_subjects=20)
-func_filenames = adhd_dataset.func
-confounds = adhd_dataset.confounds
+main_dataset = datasets.fetch_main(n_subjects=20)
+func_filenames = main_dataset.func
+confounds = main_dataset.confounds
 
 ################################################################################
 # Extract resting-state networks with DictionaryLearning
@@ -45,7 +45,7 @@
 from nilearn.decomposition import DictLearning
 
 # Initialize DictLearning object
-dict_learn = DictLearning(n_components=5, smoothing_fwhm=6.,
+dict_learn = DictLearning(n_components=8, smoothing_fwhm=6.,
                           memory="nilearn_cache", memory_level=2,
                           random_state=0)
 # Fit to the data
@@ -87,7 +87,7 @@
 # Visualization of region extraction results
 title = ('%d regions are extracted from %d components.'
          '\nEach separate color of region indicates extracted region'
-         % (n_regions_extracted, 5))
+         % (n_regions_extracted, 8))
 plotting.plot_prob_atlas(regions_extracted_img, view_type='filled_contours',
                          title=title)