docs: fix some typos

Author: szhorvat

src/_igraph/graphobject.c

@@ -1925,8 +1925,8 @@ PyObject *igraphmodule_Graph_radius(igraphmodule_GraphObject * self,
 }
 
 /** \ingroup python_interface_graph
- * \brief Converts a tree graph into a Prufer sequence
- * \return the Prufer sequence as a Python object
+ * \brief Converts a tree graph into a Prüfer sequence
+ * \return the Prüfer sequence as a Python object
  * \sa igraph_to_prufer
  */
 PyObject *igraphmodule_Graph_to_prufer(igraphmodule_GraphObject *self, PyObject* Py_UNUSED(_null))
@@ -2024,7 +2024,7 @@ PyObject *igraphmodule_Graph_Atlas(PyTypeObject * type, PyObject * args)
 }
 
 /** \ingroup python_interface_graph
- * \brief Generates a graph based on the Barabasi-Albert model
+ * \brief Generates a graph based on the Barabási-Albert model
  * This is intended to be a class method in Python, so the first argument
  * is the type object and not the Python igraph object (because we have
  * to allocate that in this method).
@@ -3290,7 +3290,7 @@ PyObject *igraphmodule_Graph_Random_Bipartite(PyTypeObject * type,
 }
 
 /** \ingroup python_interface_graph
- * \brief Generates a graph based on sort of a "windowed" Barabasi-Albert model
+ * \brief Generates a graph based on sort of a "windowed" Barabási-Albert model
  * \return a reference to the newly generated Python igraph object
  * \sa igraph_recent_degree_game
  */
@@ -13864,8 +13864,8 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    METH_VARARGS | METH_CLASS | METH_KEYWORDS,
    "Barabasi(n, m, outpref=False, directed=False, power=1,\n"
    "         zero_appeal=1, implementation=\"psumtree\", start_from=None)\n--\n\n"
-   "Generates a graph based on the Barabasi-Albert model.\n\n"
-   "B{Reference}: Barabasi, A-L and Albert, R. 1999. Emergence of scaling\n"
+   "Generates a graph based on the Barabási-Albert model.\n\n"
+   "B{Reference}: Barabási, A-L and Albert, R. 1999. Emergence of scaling\n"
    "in random networks. I{Science}, 286 509-512.\n\n"
    "@param n: the number of vertices\n"
    "@param m: either the number of outgoing edges generated for\n"
@@ -14084,7 +14084,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    METH_VARARGS | METH_CLASS | METH_KEYWORDS,
    "Preference(n, type_dist, pref_matrix, attribute=None, directed=False, loops=False)\n--\n\n"
    "Generates a graph based on vertex types and connection probabilities.\n\n"
-   "This is practically the nongrowing variant of L{Establishment}.\n"
+   "This is practically the non-growing variant of L{Establishment}.\n"
    "A given number of vertices are generated. Every vertex is assigned to a\n"
    "vertex type according to the given type probabilities. Finally, every\n"
    "vertex pair is evaluated and an edge is created between them with a\n"
@@ -14140,7 +14140,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
   {"SBM", (PyCFunction) igraphmodule_Graph_SBM,
    METH_VARARGS | METH_CLASS | METH_KEYWORDS,
    "SBM(n, pref_matrix, block_sizes, directed=False, loops=False)\n--\n\n"
-   "Generates a graph based on a stochastic blockmodel.\n\n"
+   "Generates a graph based on a stochastic block model.\n\n"
    "A given number of vertices are generated. Every vertex is assigned to a\n"
    "vertex type according to the given block sizes. Vertices of the same\n"
    "type will be assigned consecutive vertex IDs. Finally, every\n"
@@ -14494,7 +14494,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "of the vertices.\n\n"
    "This coefficient is basically the correlation between the actual\n"
    "connectivity patterns of the vertices and the pattern expected from the\n"
-   "disribution of the vertex types.\n\n"
+   "distribution of the vertex types.\n\n"
    "See equation (21) in Newman MEJ: Mixing patterns in networks, Phys Rev E\n"
    "67:026126 (2003) for the proper definition. The actual calculation is\n"
    "performed using equation (26) in the same paper for directed graphs, and\n"
@@ -14684,7 +14684,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "closeness(vertices=None, mode=\"all\", cutoff=None, weights=None, "
    "normalized=True)\n--\n\n"
    "Calculates the closeness centralities of given vertices in a graph.\n\n"
-   "The closeness centerality of a vertex measures how easily other\n"
+   "The closeness centrality of a vertex measures how easily other\n"
    "vertices can be reached from it (or the other way: how easily it\n"
    "can be reached from the other vertices). It is defined as the\n"
    "number of vertices minus one divided by the sum of\n"
@@ -14717,7 +14717,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    "harmonic_centrality(vertices=None, mode=\"all\", cutoff=None, weights=None, "
    "normalized=True)\n--\n\n"
    "Calculates the harmonic centralities of given vertices in a graph.\n\n"
-   "The harmonic centerality of a vertex measures how easily other\n"
+   "The harmonic centrality of a vertex measures how easily other\n"
    "vertices can be reached from it (or the other way: how easily it\n"
    "can be reached from the other vertices). It is defined as the\n"
    "mean inverse distance to all other vertices.\n\n"
@@ -14744,7 +14744,7 @@ struct PyMethodDef igraphmodule_Graph_methods[] = {
    METH_VARARGS | METH_KEYWORDS,
    "connected_components(mode=\"strong\")\n--\n\n"
    "Calculates the (strong or weak) connected components for a given graph.\n\n"
-   "Atttention: this function has a more convenient interface in class\n"
+   "Attention: this function has a more convenient interface in class\n"
    "L{Graph}, which wraps the result in a L{VertexClustering} object.\n"
    "It is advised to use that.\n"
    "@param mode: must be either C{\"strong\"} or C{\"weak\"}, depending on\n"

src/igraph/adjacency.py

@@ -128,7 +128,7 @@ def _get_adjlist(self, mode="out"):
     contains the neighbors of the given vertex.
 
     @param mode: if C{\"out\"}, returns the successors of the vertex. If
-      C{\"in\"}, returns the predecessors of the vertex. If C{\"all"\"}, both
+      C{\"in\"}, returns the predecessors of the vertex. If C{\"all\"}, both
       the predecessors and the successors will be returned. Ignored
       for undirected graphs.
     """

src/igraph/basic.py

@@ -34,7 +34,7 @@ def _add_edges(graph, es, attributes=None):
     @param es: the list of edges to be added. Every edge is represented
       with a tuple containing the vertex IDs or names of the two
       endpoints. Vertices are enumerated from zero.
-    @param attributes: dict of sequences, all of length equal to the
+    @param attributes: dict of sequences, each of length equal to the
       number of edges to be added, containing the attributes of the new
       edges.
     """
@@ -92,7 +92,7 @@ def _add_vertices(graph, n, attributes=None):
       vertex to be added, or a sequence of strings, each corresponding to the
       name of a vertex to be added. Names will be assigned to the C{name}
       vertex attribute.
-    @param attributes: dict of sequences, all of length equal to the
+    @param attributes: dict of sequences, each of length equal to the
       number of vertices to be added, containing the attributes of the new
       vertices. If n is a string (so a single vertex is added), then the
       values of this dict are the attributes themselves, but if n=1 then
@@ -136,7 +136,7 @@ def _delete_edges(graph, *args, **kwds):
     first positional argument is callable, an edge sequence is derived by
     calling L{EdgeSeq.select} with the same positional and keyword
     arguments. Edges in the derived edge sequence will be removed.
-    Otherwise the first positional argument is considered as follows:
+    Otherwise, the first positional argument is considered as follows:
 
     Deprecation notice: C{delete_edges(None)} has been replaced by
     C{delete_edges()} - with no arguments - since igraph 0.8.3.

src/igraph/clustering.py

@@ -452,7 +452,7 @@ def __plot__(self, backend, context, *args, **kwds):
 
               - C{True}: all the groups will be highlighted, the colors matching
                 the corresponding color indices from the current palette
-                (see the C{palette} keyword argument of L{Graph.__plot__}.
+                (see the C{palette} keyword argument of L{Graph.__plot__}).
 
               - A dict mapping cluster indices or tuples of vertex indices to
                 color names.  The given clusters or vertex groups will be
@@ -1151,7 +1151,7 @@ def __plot__(self, backend, context, *args, **kwds):
 
               - C{True}: all the clusters will be highlighted, the colors matching
                 the corresponding color indices from the current palette
-                (see the C{palette} keyword argument of L{Graph.__plot__}.
+                (see the C{palette} keyword argument of L{Graph.__plot__}).
 
               - A dict mapping cluster indices or tuples of vertex indices to
                 color names.  The given clusters or vertex groups will be
@@ -1371,7 +1371,7 @@ def _handle_mark_groups_arg_for_clustering(mark_groups, clustering):
     to clusters automatically.
     """
     # Handle the case of mark_groups = True, mark_groups containing a list or
-    # tuple of cluster IDs, and and mark_groups yielding (cluster ID, color)
+    # tuple of cluster IDs, and mark_groups yielding (cluster ID, color)
     # pairs
     if mark_groups is True:
         group_iter = ((group, color) for color, group in enumerate(clustering))
@@ -1483,7 +1483,7 @@ def compare_communities(comm1, comm2, method="vi", remove_none=False):
       as a L{Clustering} object.
     @param method: the measure to use. C{"vi"} or C{"meila"} means the
       variation of information metric of Meila (2003), C{"nmi"} or C{"danon"}
-      means the normalized mutual information as defined by Danon et al (2005),
+      means the normalized mutual information as defined by Danon et al. (2005),
       C{"split-join"} means the split-join distance of van Dongen (2000),
       C{"rand"} means the Rand index of Rand (1971), C{"adjusted_rand"}
       means the adjusted Rand index of Hubert and Arabie (1985).

src/igraph/community.py

@@ -46,7 +46,7 @@ def _community_infomap(graph, edge_weights=None, vertex_weights=None, trials=10)
 
     @param edge_weights: name of an edge attribute or a list containing
       edge weights.
-    @param vertex_weights: name of an vertex attribute or a list containing
+    @param vertex_weights: name of a vertex attribute or a list containing
       vertex weights.
     @param trials: the number of attempts to partition the network.
     @return: an appropriate L{VertexClustering} object with an extra attribute
@@ -111,7 +111,7 @@ def _community_label_propagation(graph, weights=None, initial=None, fixed=None):
 
     Note that since ties are broken randomly, there is no guarantee that
     the algorithm returns the same community structure after each run.
-    In fact, they frequently differ. See the paper of Raghavan et al
+    In fact, they frequently differ. See the paper of Raghavan et al.
     on how to come up with an aggregated community structure.
 
     Also note that the community _labels_ (numbers) have no semantic meaning
@@ -156,10 +156,10 @@ def _community_multilevel(graph, weights=None, return_levels=False, resolution=1
     iteratively in a way that maximizes the vertices' local contribution
     to the overall modularity score. When a consensus is reached (i.e. no
     single move would increase the modularity score), every community in
-    the original graph is shrank to a single vertex (while keeping the
+    the original graph is shrunk to a single vertex (while keeping the
     total weight of the incident edges) and the process continues on the
     next level. The algorithm stops when it is not possible to increase
-    the modularity any more after shrinking the communities to vertices.
+    the modularity anymore after shrinking the communities to vertices.
 
     This algorithm is said to run almost in linear time on sparse graphs.
 

src/igraph/configuration.py

@@ -125,7 +125,7 @@ def setboolean(obj, section, key, value):
             @param value: the value itself. C{0}, C{false}, C{no} and C{off}
               means false, C{1}, C{true}, C{yes} and C{on} means true,
               everything else results in a C{ValueError} being thrown.
-              Values are case insensitive
+              Values are case-insensitive
             """
             value = str(value).lower()
             if value in ("0", "false", "no", "off"):
@@ -234,7 +234,7 @@ def _item_to_section_key(item: str) -> Tuple[str, str]:
         """Converts an item description to a section-key pair.
 
         @param item: the item to be converted
-        @return: if C{item} contains a period (C{.}), it is splitted into two parts
+        @return: if C{item} contains a period (C{.}), it is split into two parts
           at the first period, then the two parts are returned, so the part before
           the period is the section. If C{item} does not contain a period, the
           section is assumed to be C{general}, and the second part of the returned

src/igraph/formula.py

@@ -153,7 +153,7 @@ def construct_graph_from_formula(cls, formula=None, attr="name", simplify=True):
       + edges (vertex names):
       A->B
 
-    If you have may disconnected componnets, you can separate them
+    If you have many disconnected componnets, you can separate them
     with commas. You can also specify isolated vertices:
 
       >>> g = Graph.Formula("A--B, C--D, E--F, G--H, I, J, K")
@@ -181,7 +181,7 @@ def construct_graph_from_formula(cls, formula=None, attr="name", simplify=True):
     @param formula: the formula itself
     @param attr: name of the vertex attribute where the vertex names
                  will be stored
-    @param simplify: whether the simplify the constructed graph
+    @param simplify: whether to simplify the constructed graph
     @return: the constructed graph:
     """
 

src/igraph/layout.py

@@ -38,7 +38,7 @@ class Layout:
     the vertices. It was particularly convenient for me to use the same layout
     for all of them, especially when I made figures for a paper. However,
     C{igraph} will of course refuse to draw a graph with a layout that has
-    less coordinates than the node count of the graph.
+    fewer coordinates than the node count of the graph.
 
     Layouts behave exactly like lists when they are accessed using the item
     index operator (C{[...]}). They can even be iterated through. Items

src/igraph/statistics.py

@@ -530,7 +530,7 @@ def power_law_fit(data, xmin=None, method="auto", p_precision=0.01):
           size if n is small.
 
         - C{discrete}: exact maximum likelihood estimation when the
-          input comes from a discrete scale (see Clauset et al among the
+          input comes from a discrete scale (see Clauset et al. among the
           references).
 
         - C{auto}: exact maximum likelihood estimation where the continuous