Merge branch 'master' of https://github.com/nilearn/nilearn into ev_d…

…ecomposition * 'master' of https://github.com/nilearn/nilearn: Fix Flake8 errors overlooked when merging PR nilearn#2028 (Plot connectome strength) (nilearn#2174) ENH: add connectome strength plot (nilearn#2028) Fix cache mixin tests (nilearn#2161) Do not fail if metadata cannot be updated for an image (nilearn#2167) [MRG] Nans in view connectome (nilearn#2166)
KamalakerDadi · Oct 14, 2019 · 251778b · 251778b
2 parents 259783f + bc6a019
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diff --git a/doc/modules/reference.rst b/doc/modules/reference.rst
@@ -324,6 +324,7 @@ uses.
    plot_stat_map
    plot_glass_brain
    plot_connectome
+   plot_connectome_strength
    plot_prob_atlas
    plot_surf
    plot_surf_roi

diff --git a/doc/plotting/index.rst b/doc/plotting/index.rst
@@ -45,6 +45,10 @@ different heuristics to find cutting coordinates.
      :target: ../auto_examples/03_connectivity/plot_sphere_based_connectome.html
      :scale: 50
 
+.. |plot_strength| image:: ../auto_examples/03_connectivity/images/sphx_glr_plot_sphere_based_connectome_004.png
+     :target: ../auto_examples/03_connectivity/plot_sphere_based_connectome.html
+     :scale: 50
+
 .. |plot_anat| image:: ../auto_examples/01_plotting/images/sphx_glr_plot_demo_plotting_003.png
      :target: ../auto_examples/01_plotting/plot_demo_plotting.html
      :scale: 50
@@ -102,6 +106,15 @@ different heuristics to find cutting coordinates.
                      are demonstrated in
                      **Example:** :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
 
+|plot_strength|     :func:`plot_connectome_strength`
+                     |hack|
+                     Plotting a connectome strength
+
+                     Functions for automatic extraction of coords based on
+                     brain parcellations useful for :func:`plot_connectome`
+                     are demonstrated in
+                     **Example:** :ref:`sphx_glr_auto_examples_03_connectivity_plot_atlas_comparison.py`
+
 |plot_prob_atlas|    :func:`plot_prob_atlas`
                      |hack|
                      Plotting 4D probabilistic atlas maps

diff --git a/doc/whats_new.rst b/doc/whats_new.rst
@@ -23,7 +23,9 @@ NEW
   Yields very fast & accurate models, without creation of giant
   clusters.
   :class:`nilearn.regions.ReNA`
-
+- Plot connectome strength
+  Use :func:`nilearn.plotting.plot_connectome_strength` to plot the strength of a
+  connectome on a glass brain.  Strength is absolute sum of the edges at a node.
 - Optimization to image resampling
   :func:`nilearn.image.resample_img` has been optimized to pad rather than
   resample images in the special case when there is only a translation
@@ -68,6 +70,9 @@ Changes
   colormap. These arguments were already accepted in `kwargs` but not documented
   before.
 
+- :func:`nilearn.plotting.view_connectome` now converts NaNs in the adjacency
+  matrix to 0.
+
 Fixes
 -----
 

diff --git a/examples/03_connectivity/plot_sphere_based_connectome.py b/examples/03_connectivity/plot_sphere_based_connectome.py
@@ -24,8 +24,8 @@
 covariance** and **partial_correlation**, to recover the functional brain
 **networks structure**.
 
-We'll start by extracting signals from Default Mode Network regions and computing a
-connectome from them.
+We'll start by extracting signals from Default Mode Network regions and
+computing a connectome from them.
 
 """
 
@@ -37,7 +37,7 @@
 # connectivity dataset.
 
 from nilearn import datasets
-dataset = datasets.fetch_development_fmri(n_subjects=1)
+dataset = datasets.fetch_development_fmri(n_subjects=20)
 
 # print basic information on the dataset
 print('First subject functional nifti image (4D) is at: %s' %
@@ -49,21 +49,21 @@
 # ------------------------------------
 dmn_coords = [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)]
 labels = [
-          'Posterior Cingulate Cortex',
-          'Left Temporoparietal junction',
-          'Right Temporoparietal junction',
-          'Medial prefrontal cortex',
-         ]
+    'Posterior Cingulate Cortex',
+    'Left Temporoparietal junction',
+    'Right Temporoparietal junction',
+    'Medial prefrontal cortex',
+    ]
 
 ##########################################################################
 # Extracts signal from sphere around DMN seeds
 # ----------------------------------------------
 #
-# We can compute the mean signal within **spheres** of a fixed radius 
+# We can compute the mean signal within **spheres** of a fixed radius
 # around a sequence of (x, y, z) coordinates with the object
 # :class:`nilearn.input_data.NiftiSpheresMasker`.
-# The resulting signal is then prepared by the masker object: Detrended, 
-# band-pass filtered and **standardized to 1 variance**. 
+# The resulting signal is then prepared by the masker object: Detrended,
+# band-pass filtered and **standardized to 1 variance**.
 
 from nilearn import input_data
 
@@ -73,7 +73,7 @@
     low_pass=0.1, high_pass=0.01, t_r=2,
     memory='nilearn_cache', memory_level=1, verbose=2)
 
-# Additionally, we pass confound information so ensure our extracted 
+# Additionally, we pass confound information to ensure our extracted
 # signal is cleaned from confounds.
 
 func_filename = dataset.func[0]
@@ -114,7 +114,8 @@
 # Display connectome
 # -------------------
 #
-# We display the graph of connections with `:func: nilearn.plotting.plot_connectome`.
+# We display the graph of connections with
+# `:func: nilearn.plotting.plot_connectome`.
 
 from nilearn import plotting
 
@@ -172,7 +173,8 @@
 #
 #
 #     You can retrieve the coordinates for any atlas, including atlases
-#     not included in nilearn, using :func:`nilearn.plotting.find_parcellation_cut_coords`.
+#     not included in nilearn, using
+#     :func:`nilearn.plotting.find_parcellation_cut_coords`.
 
 
 ###############################################################################
@@ -231,8 +233,8 @@
 
 
 ###############################################################################
-# Plot matrix and graph
-# ---------------------
+# Plot matrix, graph, and strength
+# --------------------------------
 #
 # We use `:func: nilearn.plotting.plot_matrix` to visualize our correlation matrix
 # and display the graph of connections with `nilearn.plotting.plot_connectome`.
@@ -247,13 +249,41 @@
 
 
 ###############################################################################
-# .. note:: 
-# 
-#     Note the 1. on the matrix diagonal: These are the signals variances, set to
-#     1. by the `spheres_masker`. Hence the covariance of the signal is a
+# .. note::
+#
+#     Note the 1. on the matrix diagonal: These are the signals variances, set
+#     to 1. by the `spheres_masker`. Hence the covariance of the signal is a
 #     correlation matrix.
 
 
+###############################################################################
+# Sometimes, the information in the correlation matrix is overwhelming and
+# aggregating edge strength from the graph would help. Use the function
+# `nilearn.plotting.plot_connectome_strength` to visualize this information.
+
+plotting.plot_connectome_strength(
+    matrix, coords, title='Connectome strength for Power atlas'
+)
+
+###############################################################################
+# From the correlation matrix, we observe that there is a positive and negative
+# structure. We could make two different plots by plotting these strengths
+# separately.
+
+from matplotlib.pyplot import cm
+
+# plot the positive part of of the matrix
+plotting.plot_connectome_strength(
+    np.clip(matrix, 0, matrix.max()), coords, cmap=cm.YlOrRd,
+    title='Strength of the positive edges of the Power correlation matrix'
+)
+
+# plot the negative part of of the matrix
+plotting.plot_connectome_strength(
+    np.clip(matrix, matrix.min(), 0), coords, cmap=cm.PuBu,
+    title='Strength of the negative edges of the Power correlation matrix'
+)
+
 ###############################################################################
 # Connectome extracted from Dosenbach's atlas
 # -------------------------------------------
@@ -283,6 +313,17 @@
 
 plotting.plot_connectome(matrix, coords, title='Dosenbach correlation graph',
                          edge_threshold="99.7%", node_size=20, colorbar=True)
+plotting.plot_connectome_strength(
+    matrix, coords, title='Connectome strength for Power atlas'
+)
+plotting.plot_connectome_strength(
+    np.clip(matrix, 0, matrix.max()), coords, cmap=cm.YlOrRd,
+    title='Strength of the positive edges of the Power correlation matrix'
+)
+plotting.plot_connectome_strength(
+    np.clip(matrix, matrix.min(), 0), coords, cmap=cm.PuBu,
+    title='Strength of the negative edges of the Power correlation matrix'
+)
 
 
 ###############################################################################

diff --git a/nilearn/datasets/neurovault.py b/nilearn/datasets/neurovault.py
@@ -1651,19 +1651,25 @@ def _update_image(image_info, download_params):
     """
     if not download_params['write_ok']:
         return image_info
-    collection = _fetch_collection_for_image(
-        image_info, download_params)
-    image_info, collection = _download_image_terms(
-        image_info, collection, download_params)
-    metadata_file_path = os.path.join(
-        os.path.dirname(image_info['absolute_path']),
-        'image_{0}_metadata.json'.format(image_info['id']))
-    _write_metadata(image_info, metadata_file_path)
+    try:
+        collection = _fetch_collection_for_image(
+            image_info, download_params)
+        image_info, collection = _download_image_terms(
+            image_info, collection, download_params)
+        metadata_file_path = os.path.join(
+            os.path.dirname(image_info['absolute_path']),
+            'image_{0}_metadata.json'.format(image_info['id']))
+        _write_metadata(image_info, metadata_file_path)
+    except OSError:
+        warnings.warn(
+            "could not update metadata for image {}, "
+            "most likely because you do not have write "
+            "permissions to its metadata file".format(image_info["id"]))
     return image_info
 
 
 def _update(image_info, collection, download_params):
-    """Update local metadata for an image and its collection."""
+    "Update local metadata for an image and its collection."""
     image_info = _update_image(image_info, download_params)
     return image_info, collection
 

diff --git a/nilearn/plotting/__init__.py b/nilearn/plotting/__init__.py
@@ -37,7 +37,7 @@ def _set_mpl_backend():
 from . import cm
 from .img_plotting import plot_img, plot_anat, plot_epi, \
     plot_roi, plot_stat_map, plot_glass_brain, plot_connectome, \
-    plot_prob_atlas, show
+    plot_connectome_strength, plot_prob_atlas, show
 from .find_cuts import find_xyz_cut_coords, find_cut_slices, \
     find_parcellation_cut_coords, find_probabilistic_atlas_cut_coords
 from .matrix_plotting import plot_matrix
@@ -48,7 +48,7 @@ def _set_mpl_backend():
 
 __all__ = ['cm', 'plot_img', 'plot_anat', 'plot_epi',
            'plot_roi', 'plot_stat_map', 'plot_glass_brain',
-           'plot_connectome', 'plot_prob_atlas',
+           'plot_connectome_strength', 'plot_connectome', 'plot_prob_atlas',
            'find_xyz_cut_coords', 'find_cut_slices',
            'show', 'plot_matrix', 'view_surf', 'view_img_on_surf',
            'view_img', 'view_connectome', 'view_markers',

diff --git a/nilearn/plotting/html_connectome.py b/nilearn/plotting/html_connectome.py
@@ -31,7 +31,7 @@ def _get_connectome(adjacency_matrix, coords, threshold=None,
                     marker_size=None, cmap=cm.cold_hot, symmetric_cmap=True):
     connectome = {}
     coords = np.asarray(coords, dtype='<f4')
-    adjacency_matrix = adjacency_matrix.copy()
+    adjacency_matrix = np.nan_to_num(adjacency_matrix, copy=True)
     colors = colorscale(
         cmap, adjacency_matrix.ravel(), threshold=threshold,
         symmetric_cmap=symmetric_cmap)