fix dd field typos

pymathics/graph/base.py

@@ -911,7 +911,7 @@ class DirectedEdge(GenericDirectedEdge):
 
 
     <dl>
-      <dt>'DirectedEdge[$u$, $v$]'
+      <dt>'DirectedEdge'[$u$, $v$]
       <dd>create a directed edge from $u$ to $v$.
     </dl>
 
@@ -941,7 +941,7 @@ class EdgeConnectivity(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/EdgeConnectivity.html</url>)
 
     <dl>
-      <dt>'EdgeConnectivity[$g$]'
+      <dt>'EdgeConnectivity'[$g$]
       <dd>gives the edge connectivity of the graph $g$.
     </dl>
 
@@ -1128,9 +1128,9 @@ class FindVertexCut(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/FindVertexCut.html</url>)
 
     <dl>
-    <dt>'FindVertexCut[$g$]'
+    <dt>'FindVertexCut'[$g$]
         <dd>finds a set of vertices of minimum cardinality that, if removed, renders $g$ disconnected.
-    <dt>'FindVertexCut[$g$, $s$, $t$]'
+    <dt>'FindVertexCut'[$g$, $s$, $t$]
         <dd>finds a vertex cut that disconnects all paths from $s$ to $t$.
     </dl>
 
@@ -1185,12 +1185,12 @@ class GraphAtom(AtomBuiltin):
     <url>:Graph:https://en.wikipedia.org/wiki/graph</url> (<url>:WMA:
     https://reference.wolfram.com/language/ref/Graph.html</url>)
     <dl>
-      <dt>'Graph[{$e1, $e2, ...}]'
+      <dt>'Graph'[{$e1, $e2, ...}]
       <dd>returns a graph with edges $e_j$.
     </dl>
 
     <dl>
-      <dt>'Graph[{v1, v2, ...}, {$e1, $e2, ...}]'
+      <dt>'Graph'[{v1, v2, ...}, {$e1, $e2, ...}]
       <dd>returns a graph with vertices $v_i$ and edges $e_j$.
     </dl>
 
@@ -1491,7 +1491,7 @@ class VertexList(_PatternList):
     :WMA link:
     https://reference.wolfram.com/language/ref/VertexList.html</url>
     <dl>
-      <dt>'VertexList[$edgelist$]'
+      <dt>'VertexList'[$edgelist$]
       <dd>list the vertices from a list of directed edges.
     </dl>
 
@@ -1521,7 +1521,7 @@ class UndirectedEdge(GenericUndirectedEdge):
     https://reference.wolfram.com/language/ref/UndirectedEdge.html</url>
 
     <dl>
-      <dt>'UndirectedEdge[$u$, $v$]'
+      <dt>'UndirectedEdge'[$u$, $v$]
       <dd>create an undirected edge between $u$ and $v$.
     </dl>
 

pymathics/graph/components.py

@@ -68,7 +68,7 @@ def eval(
 # class FindHamiltonianPath(_NetworkXBuiltin):
 #     """
 #     <dl>
-#       <dt>'FindHamiltonianPath[$g$]'
+#       <dt>'FindHamiltonianPath'[$g$]
 #       <dd>returns a Hamiltonian path in the given tournament graph.
 #       </dl>
 #

pymathics/graph/curated.py

@@ -15,7 +15,7 @@ class GraphData(_NetworkXBuiltin):
     :WMA link:https://reference.wolfram.com/language/ref/GraphData.html</url>
 
     <dl>
-      <dt>'GraphData[$name$]'
+      <dt>'GraphData'[$name$]
       <dd>Returns a graph with the specified name.
     </dl>
 

pymathics/graph/measures_and_metrics.py

@@ -61,13 +61,13 @@ class EdgeCount(_PatternCount):
     https://reference.wolfram.com/language/ref/EdgeCount.html</url>
 
     <dl>
-       <dt>'EdgeCount[$g$]'
+       <dt>'EdgeCount'[$g$]
        <dd>returns a count of the number of edges in graph $g$.
 
-       <dt>'EdgeCount[$g$, $patt$]'
+       <dt>'EdgeCount'[$g$, $patt$]
        <dd>returns the number of edges that match the pattern $patt$.
 
-       <dt>'EdgeCount[{$v$->$w$}, ...}, ...]'
+       <dt>'EdgeCount'[{$v$->$w$}, ...}, ...]
        <dd>uses rules $v$->$w$ to specify the graph $g$.
     </dl>
 
@@ -96,12 +96,12 @@ class GraphDistance(_NetworkXBuiltin):
     </dl>
 
     <dl>
-      <dt>'GraphDistance[$g$, $s$]'
+      <dt>'GraphDistance'[$g$, $s$]
       <dd>returns the distance from source vertex $s$ to all vertices in the graph $g$.
     </dl>
 
     <dl>
-      <dt>'GraphDistance[{$v$->$w$, ...}, ...]'
+      <dt>'GraphDistance'[{$v$->$w$, ...}, ...]
       <dd>use rules $v$->$w$ to specify the graph $g$.
     </dl>
 
@@ -169,13 +169,13 @@ class VertexCount(_PatternCount):
     https://reference.wolfram.com/language/ref/VertexCount.html</url>
 
     <dl>
-       <dt>'VertexCount[$g$]'
+       <dt>'VertexCount'[$g$]
        <dd>returns a count of the number of vertices in graph $g$.
 
-       <dt>'VertexCount[$g$, $patt$]'
+       <dt>'VertexCount'[$g$, $patt$]
        <dd>returns the number of vertices that match the pattern $patt$.
 
-       <dt>'VertexCount[{$v$->$w$}, ...}, ...]'
+       <dt>'VertexCount'[{$v$->$w$}, ...}, ...]
        <dd>uses rules $v$->$w$ to specify the graph $g$.
     </dl>
 
@@ -209,13 +209,13 @@ class VertexDegree(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/VertexDegree.html</url>
 
     <dl>
-       <dt>'VertexDegree[$g$]'
+       <dt>'VertexDegree'[$g$]
        <dd>returns a list of the degrees of each of the vertices in graph $g$.
 
-       <dt>'EdgeCount[$g$, $patt$]'
+       <dt>'EdgeCount'[$g$, $patt$]
        <dd>returns the number of edges that match the pattern $patt$.
 
-       <dt>'EdgeCount[{$v$->$w$}, ...}, ...]'
+       <dt>'EdgeCount'[{$v$->$w$}, ...}, ...]
        <dd>uses rules $v$->$w$ to specify the graph $g$.
     </dl>
 

pymathics/graph/parametric.py

@@ -35,7 +35,7 @@ class BalancedTree(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/BalancedTree.html</url>
 
     <dl>
-      <dt>'BalancedTree[$r$, $h$]'
+      <dt>'BalancedTree'[$r$, $h$]
       <dd>Returns the perfectly balanced $r$-ary tree of height $h$.
 
       In this tree produced, all non-leaf nodes will have $r$ children and \
@@ -90,7 +90,7 @@ class BarbellGraph(_NetworkXBuiltin):
     https://mathworld.wolfram.com/BarbellGraph.html</url>)
 
     <dl>
-      <dt>'BarbellGraph[$m1$, $m2$]'
+      <dt>'BarbellGraph'[$m_1$, $m_2$]
       <dd>Barbell Graph: two complete graphs connected by a path.
     </dl>
 
@@ -148,7 +148,7 @@ class BinomialTree(_NetworkXBuiltin):
     :WMA:https://reference.wolfram.com/language/ref/BinomialTree.html</url>)
 
     <dl>
-      <dt>'BinomialTree[$n$]'
+      <dt>'BinomialTree'[$n$]
       <dd>Returns the Binomial Tree of order $n$.
 
       The binomial tree of order $n$ with root $R$ is defined as:
@@ -206,7 +206,7 @@ class CompleteGraph(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/CompleteGraph.html</url>)
 
     <dl>
-      <dt>'CompleteGraph[$n$]'
+      <dt>'CompleteGraph'[$n$]
       <dd>Returns the complete graph with $n$ vertices, $K_n$.
     </dl>
 
@@ -244,7 +244,7 @@ class CompleteKaryTree(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/CompleteKaryTree.html</url>)
 
     <dl>
-      <dt>'CompleteKaryTree[$n$, $k$]'
+      <dt>'CompleteKaryTree'[$n$, $k$]
       <dd>Creates a complete $k$-ary tree of $n$ levels.
     </dl>
 
@@ -295,7 +295,7 @@ class CycleGraph(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/CycleGraph.html</url>)
 
     <dl>
-      <dt>'CycleGraph[$n$]'
+      <dt>'CycleGraph'[$n$]
       <dd>Returns the cycle graph with $n$ vertices $C_n$.
     </dl>
 
@@ -324,7 +324,7 @@ class GraphAtlas(_NetworkXBuiltin):
     </url>
 
     <dl>
-      <dt>'GraphAtlas[$n$]'
+      <dt>'GraphAtlas'[$n$]
       <dd>Returns graph number $i$ from the NetworkX's Graph \
       Atlas. There are about 1200 of them and get large as $i$ \
       increases.
@@ -368,7 +368,7 @@ class HknHararyGraph(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/HknHararyGraph.html</url>
 
     <dl>
-      <dt>'HknHararyGraph[$k$, $n$]'
+      <dt>'HknHararyGraph'[$k$, $n$]
       <dd>Returns the Harary graph with given node connectivity and node number.
 
       This second generator gives the Harary graph that minimizes the \
@@ -405,7 +405,7 @@ class HmnHararyGraph(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/HmnHararyGraph.html</url>
 
     <dl>
-      <dt>'HmnHararyGraph[$m$, $n$]'
+      <dt>'HmnHararyGraph'[$m$, $n$]
       <dd>Returns the Harary graph with given numbers of nodes and edges.
 
       This generator gives the Harary graph that maximizes the node \
@@ -461,12 +461,12 @@ class KaryTree(_NetworkXBuiltin):
 
 
     <dl>
-      <dt>'KaryTree[$r$, $n$]'
+      <dt>'KaryTree'[$r$, $n$]
       <dd>Creates binary tree of $n$ vertices.
     </dl>
 
     <dl>
-      <dt>'KaryTree[$n$, $k$]'
+      <dt>'KaryTree'[$n$, $k$]
       <dd>Creates $k$-ary tree with $n$ vertices.
     </dl>
 
@@ -509,7 +509,7 @@ class LadderGraph(_NetworkXBuiltin):
 /generated/networkx.generators.classic.ladder_graph.html</url>)
 
     <dl>
-      <dt>'LadderGraph[$n$]'
+      <dt>'LadderGraph'[$n$]
       <dd>Returns the Ladder graph of length $n$.
     </dl>
 
@@ -549,7 +549,7 @@ class PathGraph(_NetworkXBuiltin):
     </url> (<url>:WMA:https://reference.wolfram.com/language/ref/PathGraph.html
     </url>)
     <dl>
-      <dt>'PathGraph[{$v_1$, $v_2$, ...}]'
+      <dt>'PathGraph'[{$v_1$, $v_2$, ...}]
       <dd>Returns a Graph with a path with vertices $v_i$ and \
       edges between $v-i$ and $v_i+1$ .
     </dl>
@@ -584,7 +584,7 @@ class RandomTree(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/RandomTree.html</url>
 
     <dl>
-      <dt>'RandomTree[$n$]'
+      <dt>'RandomTree'[$n$]
       <dd>Returns a uniformly random tree on $n$ nodes.
     </dl>
 
@@ -630,7 +630,7 @@ class StarGraph(_NetworkXBuiltin):
     https://reference.wolfram.com/language/ref/StarGraph.html
     </url>)
     <dl>
-      <dt>'StarGraph[$n$]'
+      <dt>'StarGraph'[$n$]
       <dd>Returns a star graph with $n$ vertices.
     </dl>
 

pymathics/graph/random.py

@@ -26,10 +26,10 @@ class RandomGraph(_NetworkXBuiltin):
     </url>
 
     <dl>
-      <dt>'RandomGraph[{$n$, $m$}]'
+      <dt>'RandomGraph'[{$n$, $m$}]
       <dd>Returns a pseudorandom graph with $n$ vertices and $m$ edges.
 
-      <dt>'RandomGraph[{$n$, $m$}, $k$]'
+      <dt>'RandomGraph'[{$n$, $m$}, $k$]
       <dd>Returns list of $k$ RandomGraph[{$n$, $m$}].
     </dl>
     """

pymathics/graph/structured.py

@@ -18,7 +18,7 @@ class PathGraph(_NetworkXBuiltin):
     </url>
 
     <dl>
-      <dt>'PathGraph[{$v_1$, $v_2$, ...}]'
+      <dt>'PathGraph'[{$v_1$, $v_2$, ...}]
       <dd>Returns a Graph with a path with vertices $v_i$ and edges between $v-i$ and $v_i+1$ .
     </dl>
 
@@ -50,7 +50,7 @@ class TreeGraph(Graph):
     </url>
 
     <dl>
-      <dt>'TreeGraph[{$edge_1$, $edge_2$, ...}]'
+      <dt>'TreeGraph'[{$edge_1$, $edge_2$, ...}]
       <dd>create a tree-like from a list of edges.
     </dl>
 

pymathics/graph/tree.py

@@ -87,7 +87,7 @@ class TreeGraphQ(_NetworkXBuiltin):
     (<url>:WMA:https://reference.wolfram.com/language/ref/TreeGraphQ.html</url>)
 
     <dl>
-      <dt>'TreeGraphQ[$g$]'
+      <dt>'TreeGraphQ'[$g$]
       <dd>returns $True$ if the graph $g$ is a tree and $False$ otherwise.
     </dl>