Merge pull request #1053 from PetholzA/feature/documentation_angus

Python documentation: fixes inconsistencies, typos

docs/conf.py

@@ -174,6 +174,7 @@
 
 # The name for this set of Sphinx documents.  If None, it defaults to
 # "<project> v<release> documentation".
+
 html_title = None
 
 html_add_permalinks = None
@@ -318,4 +319,4 @@
 
 # -- Options for Bibtex output -------------------------------------------------
 
-bibtex_bibfiles = ['refs.bib']
+bibtex_bibfiles = ['refs.bib']

include/networkit/graph/Graph.hpp

@@ -2088,7 +2088,7 @@ class Graph final {
      * Get an iterable range over the neighbors of @a.
      *
      * @param u Node.
-     * @return Iterator range over the neighbors of @a.
+     * @return Iterator range over the neighbors of @a u.
      */
     NeighborRange<false> neighborRange(node u) const {
         assert(exists[u]);
@@ -2100,7 +2100,7 @@ class Graph final {
      * weights.
      *
      * @param u Node.
-     * @return Iterator range over pairs of neighbors of @a and corresponding
+     * @return Iterator range over pairs of neighbors of @a u and corresponding
      * edge weights.
      */
     NeighborWeightRange<false> weightNeighborRange(node u) const {
@@ -2113,7 +2113,7 @@ class Graph final {
      * Get an iterable range over the in-neighbors of @a.
      *
      * @param u Node.
-     * @return Iterator range over pairs of in-neighbors of @a.
+     * @return Iterator range over pairs of in-neighbors of @a u.
      */
     NeighborRange<true> inNeighborRange(node u) const {
         assert(isDirected());
@@ -2126,7 +2126,7 @@ class Graph final {
      * edge weights.
      *
      * @param u Node.
-     * @return Iterator range over pairs of in-neighbors of @a and corresponding
+     * @return Iterator range over pairs of in-neighbors of @a u and corresponding
      * edge weights.
      */
     NeighborWeightRange<true> weightInNeighborRange(node u) const {

networkit/algebraic.py

@@ -87,14 +87,12 @@ def laplacianMatrix(G):
 	Parameters
 	----------
 	G : networkit.Graph
-		The input graph.
+		The graph.
 
 	Returns
 	-------
-	ndarray or :py:class:`scipy.sparse.csr_matrix`
-    	The N x N laplacian matrix of csgraph. It will be a NumPy array (dense) if the input was dense, or a sparse matrix otherwise.
-	ndarray
-    	The length-N diagonal of the Laplacian matrix. For the normalized Laplacian, this is the array of square roots of vertex degrees or 1 if the degree is zero.
+	:py:class:`scipy.sparse.csr_matrix`
+    	The N x N laplacian matrix of csgraph. It will be a scipy.sparse.csr_matrix.
 	"""
 	A = adjacencyMatrix(G)
 	return scipy.sparse.csgraph.laplacian(A)
@@ -225,7 +223,7 @@ def laplacianEigenvectors(G, cutoff=-1, reverse=False):
 
 	Parameters
 	----------
-	G : networkit.graph
+	G : networkit.Graph
 		The input graph.
 	cutoff : int, optional
 		The maximum (or minimum) number of eigenvectors needed. Default: -1
@@ -252,7 +250,7 @@ def adjacencyEigenvectors(G, cutoff=-1, reverse=False):
 
 	Parameters
 	----------
-	G : networkit.graph
+	G : networkit.Graph
 		The graph.
 	cutoff : int, optional
 		The maximum (or minimum) number of eigenvectors needed. Default: -1
@@ -279,7 +277,7 @@ def laplacianEigenvector(G, i, reverse=False):
 
 	Parameters
 	----------
-	G : networkit.graph
+	G : networkit.Graph
 		The input graph.
 	i : int
 		Computes the eigenvector and value of index i.
@@ -307,7 +305,7 @@ def adjacencyEigenvector(G, i, reverse=False):
 
 	Parameters
 	----------
-	G : networkit.graph
+	G : networkit.Graph
 		The input graph.
 	i : int
 		Computes the eigenvector and value of index i.

networkit/centrality.pyx

@@ -82,8 +82,8 @@ cdef class Centrality(Algorithm):
 
 		Returns
 		-------
-		dict(tuple(int, float))
-			A vector of pairs sorted into descending order. Each pair contains a node and the corresponding score
+		list(tuple(int, float))
+			A list of pairs sorted into descending order. Each pair contains a node and the corresponding score
 		"""
 		if self._this == NULL:
 			raise RuntimeError("Error, object not properly initialized")
@@ -193,13 +193,13 @@ cdef class ApproxBetweenness(Centrality):
 	G : networkit.Graph
 		The input graph
 	epsilon : double, optional
-		Maximum additive error
+		Maximum additive error. Default: 0.01
 	delta : double, optional
-		Probability that the values are within the error guarantee
+		Probability that the values are within the error guarantee. Default: 0.1
 	universalConstant: double, optional
 		The universal constant to be used in computing the sample size.
 		It is 1 by default. Some references suggest using 0.5, but there
-		is no guarantee in this case.
+		is no guarantee in this case. Default: 1.0
 	"""
 
 	def __cinit__(self, Graph G, epsilon=0.01, delta=0.1, universalConstant=1.0):
@@ -257,7 +257,7 @@ cdef extern from "<networkit/centrality/KadabraBetweenness.hpp>":
 
 cdef class KadabraBetweenness(Algorithm):
 	"""
-	KadabraBetweenness(Graph G, err = 0.01, delta = 0.1, k = 0, unionSample = 0, startFactor = 100
+	KadabraBetweenness(Graph G, err = 0.01, delta = 0.1, k = 0, unionSample = 0, startFactor = 100)
 
 	Approximation of the betweenness centrality and computation of the top-k
 	nodes with highest betweenness centrality according to the algorithm
@@ -773,7 +773,7 @@ cdef extern from "<networkit/centrality/Closeness.hpp>":
 
 cdef class Closeness(Centrality):
 	"""
-	Closeness(G, normalized, checkConnectdedness)
+	Closeness(G, normalized, checkConnectedness)
 	Closeness(G, normalized, variant)
 
 	Constructs the Closeness class for the given Graph `G`. If the Closeness scores should not be normalized,
@@ -792,16 +792,16 @@ cdef class Closeness(Centrality):
 	normalized : bool
 		Set this parameter to False if scores should not be normalized into an interval of [0,1].
 		Normalization only works for unweighted graphs.
-	checkConnectdedness : bool
+	checkConnectedness : bool
 		Set this parameter to True to also check if the graph is connected before computing closeness.
 		Set this parameter to False to not check if the graph is connected (note: the standard definition
 		of closeness works for connected graphs, choose this if the input graph is known to be connected).
 	ClosenessVariant : networkit.centrality.ClosenessVariant
 		Set this parameter to networkit.centrality.ClosenessVariant.Standard to use the standard
-		definition of closeness, that is defined for connected graphs only; in this case, checkConnectdedness
+		definition of closeness, that is defined for connected graphs only; in this case, checkConnectedness
 		is automatically set to True.
 		Set this parameter to networkit.centrality.ClosenessVariant.Generalized to use the generalized
-		definition of closeness, that is defined for also non-connected graphs; in this case, checkConnectdedness
+		definition of closeness, that is defined for also non-connected graphs; in this case, checkConnectedness
 		is automatically set to False.
 	"""
 
@@ -2654,10 +2654,10 @@ cdef class ApproxElectricalCloseness(Centrality):
 	G : networkit.Graph
 		The input graph.
 	eps : float, optional
-		Maximum absolute error of the elements in the diagonal.
+		Maximum absolute error of the elements in the diagonal. Default: 0.1
 	kappa : float, optional
 		Balances the tolerance of the solver for the linear system and the
-		number of USTs to be sampled.
+		number of USTs to be sampled. Default: 0.3
 	"""
 
 	def __cinit__(self, Graph G, double eps = 0.1, double kappa = 0.3):
@@ -2688,7 +2688,7 @@ cdef class ApproxElectricalCloseness(Centrality):
 		Parameters
 		----------
 		tol : float
-			Tolerance for the LAMG solver.
+			Tolerance for the LAMG solver. Default: 1e-9
 
 		Returns
 		-------

networkit/community.pyx

@@ -236,17 +236,17 @@ cdef extern from "<networkit/community/GraphClusteringTools.hpp>" namespace "Net
 
 cdef class GraphClusteringTools:
 	@staticmethod
-	def getImbalance(Partition zeta, Graph graph = None):
+	def getImbalance(Partition zeta, Graph G = None):
 		"""  
-		getImbalance(zeta)
+		getImbalance(zeta, G)
 
 		Get the imbalance of clusters in the given partition.
 
 		Parameters
 		----------
 		zeta : networkit.Partition
 			The first partition.
-		graph : networkit.Graph, optional
+		G : networkit.Graph, optional
 			The input graph to compare the imbalance to. Default: None
 
 		Returns
@@ -255,7 +255,7 @@ cdef class GraphClusteringTools:
 			Imbalance of the partition.
 		"""
 		if graph is not None:
-			return getImbalance(zeta._this, graph._this)
+			return getImbalance(zeta._this, G._this)
 		else:
 			return getImbalance(zeta._this)
 
@@ -407,7 +407,10 @@ cdef class PartitionIntersection:
 	"""
 	cdef _PartitionIntersection _this
 	def calculate(self, Partition zeta, Partition eta):
-		"""  Calculate the intersection of two partitions `zeta` and `eta`
+		"""  
+		calculate(zeta, eta)
+		
+		Calculate the intersection of two partitions `zeta` and `eta`.
 
 		Parameters
 		----------
@@ -740,7 +743,7 @@ cdef class LouvainMapEquation(CommunityDetector):
 	hierarchical: bool, optional
 		Iteratively create a graph of the locally optimal clusters and optimize locally on that graph.
 	maxIterations: int, optional
-		The maximum number of local move iterations.
+		The maximum number of local move iterations. Default: 32
 	parallelizationStrategy: str, optional
 		Parallelization strategy, possible values: "relaxmap", "synchronous", "none". Default: "relaxmap"
 	"""
@@ -771,6 +774,8 @@ cdef class PLP(CommunityDetector):
 		The graph on which the algorithm has to run.
 	updateThreshold : int, optional
 		number of nodes that have to be changed in each iteration so that a new iteration starts. Default: None
+	maxIterations: int, optional
+		The maximum number of local move iterations. Default: None	
 	baseClustering : networkit.Partition, optional
 		PLP needs a base clustering to start from; if none is given the algorithm will 
 		run on a singleton clustering. Default: None

networkit/components.pyx

@@ -134,7 +134,7 @@ cdef class ConnectedComponents(ComponentDecomposition):
 
 		Parameters
 		----------
-		graph : networkit.Graph
+		G : networkit.Graph
 			The input graph.
 		compactGraph : bool, optional
 			If true, the node ids of the output graph will be compacted
@@ -163,13 +163,13 @@ cdef class ParallelConnectedComponents(ComponentDecomposition):
 
 	Parameters
 	----------
-	graph : networkit.Graph
+	G : networkit.Graph
 		The input graph
 	coarsening : bool, optional
 		Specifies whether the main algorithm based on label propagation (LP)
 		shall work recursively (true) or not (false) by coarsening/contracting
 		an LP-computed clustering. Defaults to true since we saw positive effects
-		in terms of running time for many networks. Beware of possible memory implications.
+		in terms of running time for many networks. Beware of possible memory implications. Default: True
 	"""
 
 	def __cinit__(self,  Graph G, coarsening=True	):

networkit/csbridge.py

@@ -46,7 +46,7 @@ def widget_from_graph(G, node_scores = None, node_partition = None, node_palette
 	node_palette : list of tuples, optional
 		Array consisting of normalized rgb-values. If none is given, seaborn.color_palette.colors is used.  Default: None
 	show_ids : 	boolean, optional
-		Set whether node ids should be visible in plot-widget. Is set to True by default. Default: None
+		Set whether node ids should be visible in plot-widget. Is set to True by default. Default: True
 	"""
 	print("WARNING: Module csbridge is deprecated and will be removed in future updates. Use networkit.vizbridges.widgetFromGraph() instead.")
 	return widgetFromGraph(G, nodeScores = node_scores, nodePartition = node_partition, nodePalette = node_palette, showIds = show_ids)