updated to new doc theme

conda/environments/cugraph_dev_cuda11.4.yml

@@ -31,7 +31,7 @@ dependencies:
 - faiss-proc=*=cuda
 - scikit-learn>=0.23.1
 - sphinx
-- sphinx_rtd_theme
+- pydata-sphinx-theme
 - sphinxcontrib-websupport
 - sphinx-markdown-tables
 - sphinx-copybutton

docs/cugraph/source/api_docs/structure.rst

@@ -1,31 +1,40 @@
-=========
-Structure
-=========
-.. currentmodule:: cugraph.structure.graph_classes
+=============
+Graph Classes
+=============
+.. currentmodule:: cugraph
 
-Constructor
------------
+Constructors
+------------
 .. autosummary::
    :toctree: api/
 
    Graph
+   MultiGraph
+   BiPartiteGraph
 
 
-Methods
--------
+Adding Data
+-----------
 .. autosummary::
    :toctree: api/
 
-   Graph.add_internal_vertex_id
-   Graph.add_nodes_from
-   Graph.clear
+
    Graph.from_cudf_adjlist
    Graph.from_cudf_edgelist
    Graph.from_dask_cudf_edgelist
-   Graph.from_numpy_array
-   Graph.from_numpy_matrix
    Graph.from_pandas_adjacency
    Graph.from_pandas_edgelist
+   Graph.from_numpy_array
+   Graph.from_numpy_matrix
+   Graph.add_internal_vertex_id
+   Graph.add_nodes_from
+   Graph.clear
+
+Checks
+------
+.. autosummary::
+   :toctree: api/
+
    Graph.has_isolated_vertices
    Graph.is_bipartite
    Graph.is_directed
@@ -38,7 +47,6 @@ Methods
    Graph.to_undirected
    Graph.unrenumber
 
-.. currentmodule:: cugraph
 
 Symmetrize
 ----------

docs/cugraph/source/basics/cugraph_blogs.rst

@@ -48,8 +48,3 @@ Other BLOGS
 * `Where should I walk <https://towardsdatascience.com/where-should-i-walk-e66b26735de5>`_
 * `Where really are the parking spots? <https://towardsdatascience.com/where-really-are-the-parking-spots-ed6a1129035e>`_
 * `Accelerating Single Cell Genomic Analysis using RAPIDS <https://developer.nvidia.com/blog/accelerating-single-cell-genomic-analysis-using-rapids/>`_
-
-
-
-
-Copyright (c) 2020, NVIDIA CORPORATION.

docs/cugraph/source/index.rst

@@ -1,5 +1,9 @@
 Welcome to cugraph's documentation!
 ===================================
+RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates
+into the RAPIDS data science ecosystem and allows the data scientist to
+easily call graph algorithms using data stored in GPU DataFrames,
+NetworkX Graphs, or even CuPy or SciPy sparse Matrices.
 
 .. toctree::
    :maxdepth: 2

python/cugraph/cugraph/generators/rmat.py

@@ -70,6 +70,7 @@ def _ensure_args_multi_rmat(
     """
     Ensures the args passed in are usable for the multi_rmat() API, raises the
     appropriate exception if incorrect, else returns None.
+
     """
     if not isinstance(n_edgelists, int):
         raise TypeError("'n_edgelists' must be an int")
@@ -303,9 +304,9 @@ def rmat(
         scramble_vertex_ids=True,
         create_using=None,  # return edgelist instead of Graph instance
         mg=False
-    )
-
+        )
     """
+
     _ensure_args_rmat(scale, num_edges, a, b, c, seed, clip_and_flip,
                       scramble_vertex_ids, create_using, mg)
 
@@ -335,39 +336,39 @@ def multi_rmat(
     Parameters
     ----------
     n_edgelists : int
-    Number of edge lists (graphs) to generate
+        Number of edge lists (graphs) to generate
 
     min_scale : int
-    Scale factor to set the minimum number of vertices in the graph
+        Scale factor to set the minimum number of vertices in the graph
 
     max_scale : int
-    Scale factor to set the maximum number of vertices in the graph
+        Scale factor to set the maximum number of vertices in the graph
 
     edge_factor : int
-    Average number of edges per vertex to generate
+        Average number of edges per vertex to generate
 
     size_distribution : int
-    Distribution of the graph sizes, impacts the scale parameter of the R-MAT
-    generator.
-    '0' for POWER_LAW distribution and '1' for UNIFORM distribution
+        Distribution of the graph sizes, impacts the scale parameter of the
+        R-MAT generator.
+        '0' for POWER_LAW distribution and '1' for UNIFORM distribution
 
     edge_distribution : int
-    Edges distribution for each graph, impacts how R-MAT parameters a,b,c,d,
-    are set.
-    '0' for POWER_LAW distribution and '1' for UNIFORM distribution
+        Edges distribution for each graph, impacts how R-MAT parameters
+        a,b,c,d, are set.
+        '0' for POWER_LAW distribution and '1' for UNIFORM distribution
 
     seed : int
-    Seed value for the random number generator
+        Seed value for the random number generator
 
     clip_and_flip : bool
-    Flag controlling whether to generate edges only in the lower triangular
-    part (including the diagonal) of the graph adjacency matrix
-    (if set to 'true') or not (if set to 'false')
+        Flag controlling whether to generate edges only in the lower triangular
+        part (including the diagonal) of the graph adjacency matrix
+        (if set to 'true') or not (if set to 'false')
 
     scramble_vertex_ids : bool
-    Flag controlling whether to scramble vertex ID bits (if set to 'true') or
-    not (if set to 'false'); scrambling vertx ID bits breaks correlation
-    between vertex ID values and vertex degrees
+        Flag controlling whether to scramble vertex ID bits (if set to 'true')
+        or not (if set to 'false'); scrambling vertx ID bits breaks correlation
+        between vertex ID values and vertex degrees
 
     Returns
     -------