Add enhancements to the scona

.travis.yml

@@ -9,11 +9,13 @@ env:
     secure: 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
 install:
   - pip install -q networkx==$NETWORKX_VERSION
+  - pip install .
   - pip install -q -r requirements.txt
   - pip install sphinx sphinx sphinxcontrib-napoleon sphinx_rtd_theme
 script:
   - python3 -m pytest -v --ignore=tests/regression_test.py
 # temporarily ignore regression tests while we investigate why they are failing
+  - python3 -m pytest --nbval-lax tutorials/
   - cd docs
   - make html
   - touch .nojekyll

docs/source/introduction.rst

@@ -16,7 +16,7 @@ Installing scona
 
 You can install scona directly from the GitHub repository::
 
-    pip install git+https://github.com/WhitakerLab/scona.git
+    pip install -e git+https://github.com/WhitakerLab/scona.git#egg=scona
 
 If you want to edit scona it's recommended that you pass the ``-e`` flag to ``pip`` to install the package editably.
 

docs/source/plotting.rst

@@ -1,5 +1,34 @@
 Plotting Brain Networks
 =======================
 
-With nilearn
-------------
+A good visualisation is worth millions of words. 
+That's why our community put an emphasis on ensuring that the produced figures are informative and communicate the results clearly.
+
+The :class:`scona` package has a great visualisation module, which enables you to produce publication-ready plots based on the results of the performed brain network analysis. 
+You are also able to export created figures (save as a file) to include in a research paper or in a publication.
+
+As it says the best explanation is through demonstration. That's why we have created `tutorials <https://github.com/WhitakerLab/scona/tree/master/tutorials>`_ which are jupyter notebooks that include visualisation examples of the data. 
+In a clear and easy way, these tutorials explain how different visualisation functions can be used to produce different plots and for better data understanding.
+
+1. Global Network Measures visualisation `tutorial <https://github.com/WhitakerLab/scona/blob/master/tutorials/global_measures_viz.ipynb>`_ describes how to use the following functions:
+    - plot_degree_dist() - tool for plotting the degree distribution
+    - plot_network_measures() - tool for plotting network measures values
+    - plot_rich_club() -  tool for plotting the rich club values per degree
+
+With the help of these functions, you can report measures relating to the whole network.
+
+2. Interactive visualisation `tutorial <https://github.com/WhitakerLab/scona/blob/master/tutorials/interactive_viz_tutorial.ipynb>`_ provides examples on how to use functions like:
+    - view_nodes_3d() - view the nodes on a 3d plot
+    - view_connectome_3d() - view the edges - the connections - of the network on a 3d plot
+
+These tools rely on the excellent `nilearn.plotting library <http://nilearn.github.io/plotting/index.html>`_.
+
+3. Anatomical visualisation `tutorial <https://github.com/WhitakerLab/scona/blob/master/tutorials/anatomical_viz_tutorial.ipynb>`_ shows the usage of the following functions:
+    - plot_anatomical_network() - make plots of nodes and edges based on the given anatomical layout
+    - plot_connectome () - plot connectome on top of the brain glass schematics
+
+These are static visualisions that you could use to report your findings in a published paper.
+
+
+With proper visualization, a researcher can reveal findings easier, understand complex data relationships and describe obtained insights from analyzed data.
+

docs/source/scona.rst

@@ -12,6 +12,8 @@ Subpackages
     scona.wrappers
     scona.datasets
 
+**Note**: These subpackages (scona.wrappers, scona.scripts, scona.datasets) are represented as a set of features that will be incorporated into scona package. You **should not** open or/and use the code inside these packages, because the code inside **is not supported** and this part is in the alpha development phase.
+
 Module contents
 ---------------
 

requirements.txt

@@ -10,3 +10,4 @@ scikit-learn
 networkx>=2.2
 forceatlas2
 nilearn==0.5.2
+nbval

scona/visualisations_helpers.py

@@ -488,3 +488,49 @@ def graph_to_nilearn_array(
                         "with the centroids.")
 
     return adjacency_matrix, node_coords
+
+
+def get_anatomical_layouts(G):
+    """
+    This function extracts the required coordinates of a node based on the given
+     anatomical layout.
+
+    Parameters
+    ----------
+
+    G : :class:`networkx.Graph`
+        G should have nodal locations in MNI space indexed by nodal
+        attribute "centroids".
+
+    Returns
+    -------
+    axial_dict, sagittal_dict, coronal_dict dictionaries
+        Returns three dictionaries containing appropriate pairs of coordinates
+        for axial, sagittal and coronal slices.
+    """
+
+    # create empty dictionaries to store corresponding coordinates
+    axial_dict = {}
+    sagittal_dict = {}
+    coronal_dict = {}
+
+    # check if graph has centroids
+    if G.graph["centroids"] == False:
+        raise ValueError("The given BrainNetwork Graph does not have centroids")
+
+    for node in G.nodes:
+        # get x coordinate
+        x_coord = G.node[node]["x"]
+
+        # get y coordinate
+        y_coord = G.node[node]["y"]
+
+        # get z coordinate
+        z_coord = G.node[node]["z"]
+
+        # store appropriate pairs of coordinates for sagittal, coronal and axial
+        axial_dict[node] = axial_layout(x_coord, y_coord, z_coord)
+        sagittal_dict[node] = sagittal_layout(x_coord, y_coord, z_coord)
+        coronal_dict[node] = coronal_layout(x_coord, y_coord, z_coord)
+
+    return axial_dict, sagittal_dict, coronal_dict

tutorials/global_measures_viz.ipynb

@@ -1484,9 +1484,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:kwPython3]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-kwPython3-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1498,7 +1498,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,