References to examples

Author: Titan-C

doc/conf.py

@@ -24,6 +24,7 @@
 sys.path.insert(0, os.path.abspath('sphinxext'))
 
 from github_link import make_linkcode_resolve
+import sphinx_gallery
 
 # -- General configuration ---------------------------------------------------
 
@@ -245,13 +246,11 @@
                    'sphx_glr_plot_adaboost_twoclass_001.png': 372,
                    'sphx_glr_plot_compare_methods_001.png': 349}
 
-import sphinx_gallery
 def make_carousel_thumbs(app, exception):
     """produces the final resized carousel images"""
     if exception is not None:
         return
     print('Preparing carousel images')
-    print(sphinx_gallery.__file__)
 
     image_dir = os.path.join(app.builder.outdir, '_images')
     for glr_plot, max_width in carousel_thumbs.items():

doc/modules/clustering.rst

@@ -612,10 +612,10 @@ may wish to cluster web pages by only merging pages with a link pointing
 from one to another. It can also be learned from the data, for instance
 using :func:`sklearn.neighbors.kneighbors_graph` to restrict
 merging to nearest neighbors as in :ref:`this example
-<example_cluster_plot_agglomerative_clustering.py>`, or
+<sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py>`, or
 using :func:`sklearn.feature_extraction.image.grid_to_graph` to
 enable only merging of neighboring pixels on an image, as in the
-:ref:`raccoon face <example_cluster_plot_face_ward_segmentation.py>` example.
+:ref:`raccoon face <sphx_glr_example_cluster_plot_face_ward_segmentation.py>` example.
 
 .. topic:: Examples:
 

doc/tutorial/basic/tutorial.rst

@@ -133,7 +133,7 @@ learn::
              [  0.,   0.,   6.,  13.,  10.,   0.,   0.,   0.]])
 
     The :ref:`simple example on this dataset
-    <example_classification_plot_digits_classification.py>` illustrates how starting
+    <sphx_glr_auto_examples_classification_plot_digits_classification.py>` illustrates how starting
     from the original problem one can shape the data for consumption in
     scikit-learn.
 

doc/tutorial/text_analytics/working_with_text_data.rst

@@ -547,7 +547,7 @@ upon the completion of this tutorial:
   :class:`CountVectorizer`
 
 * If you don't have labels, try using
-  :ref:`Clustering <example_text_document_clustering.py>`
+  :ref:`Clustering <sphx_glr_auto_examples_text_document_clustering.py>`
   on your problem.
 
 * If you have multiple labels per document, e.g categories, have a look
@@ -558,7 +558,7 @@ upon the completion of this tutorial:
 
 * Have a look at using
   :ref:`Out-of-core Classification
-  <example_applications_plot_out_of_core_classification.py>` to
+  <sphx_glr_auto_examples_applications_plot_out_of_core_classification.py>` to
   learn from data that would not fit into the computer main memory.
 
 * Have a look at the :ref:`Hashing Vectorizer <hashing_vectorizer>`