Minor cleanup in docs

Author: jacklinke

docs/configuration.md

@@ -16,7 +16,7 @@ The `transforms` extra installs `networkx`, `rustworkx`, and `pandas`.
 
 ## Requirements
 
-- **PostgreSQL** — required. django-postgresql-dag uses recursive CTEs (`WITH RECURSIVE`), which are PostgreSQL-specific. It is not compatible with SQLite, MySQL, or other databases.
+- **PostgreSQL** - required. django-postgresql-dag uses recursive CTEs (`WITH RECURSIVE`), which are PostgreSQL-specific. It is not compatible with SQLite, MySQL, or other databases.
 - **Supported primary key types**: `integer`, `bigint`, `uuid`. The query builders handle type casting automatically.
 
 ## Django settings
@@ -41,12 +41,12 @@ Creates an abstract base class for your Edge model.
 | Parameter | Type | Default | Description |
 |---|---|---|---|
 | `node_model` | `str` or model class | *(required)* | The Node model this edge connects. Use a string name (e.g. `"MyNode"`) when the Node class hasn't been defined yet. |
-| `concrete` | `bool` | `True` | If `True`, the factory returns a concrete model. If `False`, returns an abstract model — your subclass provides the database table. Almost always set to `False`. |
+| `concrete` | `bool` | `True` | If `True`, the factory returns a concrete model. If `False`, returns an abstract model - your subclass provides the database table. Almost always set to `False`. |
 | `base_model` | model class | `models.Model` | Base class for the generated model. Use this to inject a custom base (e.g. a TimeStampedModel). |
 
 The generated model provides:
-- `parent` — ForeignKey to the node model (the "from" side)
-- `child` — ForeignKey to the node model (the "to" side)
+- `parent` - ForeignKey to the node model (the "from" side)
+- `child` - ForeignKey to the node model (the "to" side)
 - Circular reference checking on save (unless disabled)
 - Duplicate edge checking on save (unless disabled)
 
@@ -61,7 +61,7 @@ Creates an abstract base class for your Node model.
 | `base_model` | model class | `models.Model` | Base class for the generated model. |
 
 The generated model provides:
-- `children` — ManyToManyField through the edge model
+- `children` - ManyToManyField through the edge model
 - All traversal, path, predicate, and mutation methods (see [Node API Reference](node-reference.md))
 - Custom manager with `roots()`, `leaves()`, `topological_sort()`, and other graph-wide methods
 

docs/debug.md

@@ -53,20 +53,20 @@ def debug_my_operation():
 
 These operations are captured when called inside a `log_queries` block:
 
-- `ancestors()` / `ancestors_raw()` — records `AncestorQuery`
-- `descendants()` / `descendants_raw()` — records `DescendantQuery`
-- `path()` / `path_raw()` — records `DownwardPathQuery` and/or `UpwardPathQuery`
-- `connected_graph()` / `connected_graph_raw()` — records `ConnectedGraphQuery`
-- `node_depth()` — records `node_depth`
+- `ancestors()` / `ancestors_raw()` - records `AncestorQuery`
+- `descendants()` / `descendants_raw()` - records `DescendantQuery`
+- `path()` / `path_raw()` - records `DownwardPathQuery` and/or `UpwardPathQuery`
+- `connected_graph()` / `connected_graph_raw()` - records `ConnectedGraphQuery`
+- `node_depth()` - records `node_depth`
 
 ## `DAGQueryLog` reference
 
 The object returned by `log_queries().__enter__()` is a `DAGQueryLog` instance with:
 
-- **`queries`** (`list[dict]`) — each entry has:
+- **`queries`** (`list[dict]`) - each entry has:
   - `query_class` (str): the query builder class name (e.g. `"DescendantQuery"`) or `"node_depth"`
   - `sql` (str): the formatted CTE SQL template
   - `params` (dict): the parameters passed to the query
-- **`executed`** (`list[dict]`) — only populated when `capture_executed=True`. Each entry has:
+- **`executed`** (`list[dict]`) - only populated when `capture_executed=True`. Each entry has:
   - `sql` (str): the actual SQL executed by Django
   - `time` (str): execution time in seconds

docs/edge-reference.md

@@ -22,7 +22,7 @@ These are called on `MyEdge.objects`.
 : Returns a QuerySet of edges forming the shortest path from `start_node` to `end_node`. Accepts `directional` (default `True`).
 
 **redundant_edges(self)**
-: Returns a QuerySet of redundant edges — those removable by transitive reduction. An edge A→C is redundant if C is reachable from A via a path of length >= 2.
+: Returns a QuerySet of redundant edges - those removable by transitive reduction. An edge A→C is redundant if C is reachable from A via a path of length >= 2.
 
 **transitive_reduction(self, delete=False)**
 : Identifies redundant edges. With `delete=True`, removes them and returns the count. See also `NodeManager.transitive_reduction()`.

docs/filtering.md

@@ -1,6 +1,6 @@
 # Filtering Graph Traversals
 
-All traversal methods (`ancestors()`, `descendants()`, `clan()`, `path()`, `connected_graph()`, and their variants) accept filtering parameters that control which parts of the graph are explored. These filters operate at the CTE level — excluded edges are never traversed, not just hidden from results.
+All traversal methods (`ancestors()`, `descendants()`, `clan()`, `path()`, `connected_graph()`, and their variants) accept filtering parameters that control which parts of the graph are explored. These filters operate at the CTE level - excluded edges are never traversed, not just hidden from results.
 
 ## Limiting depth with `max_depth`
 
@@ -70,7 +70,7 @@ Pass a queryset of edge instances to exclude from traversal. The CTE will skip t
 >>> node.path(target, disallowed_edges_queryset=edge_to_skip)
 ```
 
-This is useful for "what if" scenarios — exploring the graph as it would look with certain connections removed, without actually deleting them.
+This is useful for "what if" scenarios - exploring the graph as it would look with certain connections removed, without actually deleting them.
 
 ## Restricting to specific edges with `allowed_edges_queryset`
 

docs/node-reference.md

@@ -16,7 +16,7 @@ These are called on `MyNode.objects`.
 : Returns a list of QuerySets, one per disconnected subgraph. Each QuerySet can be further filtered.
 
 **graph_stats(self)**
-: Returns a dict with aggregate metrics: `node_count`, `edge_count`, `root_count`, `leaf_count`, `island_count`, `max_depth`, `avg_depth`, `density`, `component_count`. Runs O(N) queries — suitable for analytics, not hot paths.
+: Returns a dict with aggregate metrics: `node_count`, `edge_count`, `root_count`, `leaf_count`, `island_count`, `max_depth`, `avg_depth`, `density`, `component_count`. Runs O(N) queries - suitable for analytics, not hot paths.
 
 **topological_sort(self, max_depth=None)**
 : Returns a QuerySet of all nodes in topological order (parents before children). Island nodes are included at the front.
@@ -102,7 +102,7 @@ All traversal methods accept optional keyword arguments for [filtering](filterin
 ## Path Methods
 
 **path(self, ending_node, \*\*kwargs)**
-: Returns a QuerySet of the shortest path from self to `ending_node`. Sorted root-side toward leaf-side regardless of direction. Raises `NodeNotReachableException` if no path exists. Accepts `directional` (default `True`) — set to `False` to search in both directions.
+: Returns a QuerySet of the shortest path from self to `ending_node`. Sorted root-side toward leaf-side regardless of direction. Raises `NodeNotReachableException` if no path exists. Accepts `directional` (default `True`) - set to `False` to search in both directions.
 
 **path_exists(self, ending_node, \*\*kwargs)**
 : Returns `True` if a path exists from self to `ending_node`. Accepts `directional`.
@@ -114,7 +114,7 @@ All traversal methods accept optional keyword arguments for [filtering](filterin
 : Returns a list of QuerySets, each representing one path. Unlike `path()`, returns all paths, not just the shortest.
 
 **all_paths_as_pk_lists(self, ending_node, directional=True, max_results=None, \*\*kwargs)**
-: Returns a list of PK lists — lightweight alternative to `all_paths()`.
+: Returns a list of PK lists - lightweight alternative to `all_paths()`.
 
 **weighted_path(self, ending_node, weight_field="weight", \*\*kwargs)**
 : Returns a `(QuerySet, total_weight)` tuple for the minimum-weight path. The edge model must have the specified weight field. Raises `NodeNotReachableException` or `WeightFieldDoesNotExistException`.

docs/paths-and-algorithms.md

@@ -1,6 +1,6 @@
 # Working with Paths and Algorithms
 
-Beyond basic ancestor/descendant traversal, django-postgresql-dag provides several graph algorithms — all implemented as PostgreSQL recursive CTEs or using the NetworkX library.
+Beyond basic ancestor/descendant traversal, django-postgresql-dag provides several graph algorithms - all implemented as PostgreSQL recursive CTEs or using the NetworkX library.
 
 The examples on this page use the graph from the [Quickstart](quickstart.md):
 
@@ -62,7 +62,7 @@ For lightweight processing, `all_paths_as_pk_lists()` returns lists of primary k
 [[1, 4, 7, 9], [1, 4, 8, 9]]
 ```
 
-Use `max_results` to cap the number of paths returned — useful in dense graphs:
+Use `max_results` to cap the number of paths returned - useful in dense graphs:
 
 ```python
 >>> root.all_paths(c1, max_results=1)
@@ -120,7 +120,7 @@ At the instance level, get a node and its descendants in topological order:
 
 ## Critical path
 
-The critical path is the longest weighted path from any root to any leaf in the DAG. This is useful for project scheduling — the critical path determines the minimum total duration.
+The critical path is the longest weighted path from any root to any leaf in the DAG. This is useful for project scheduling - the critical path determines the minimum total duration.
 
 ```python
 >>> path_qs, total_weight = NetworkNode.objects.critical_path(weight_field="weight")
@@ -175,7 +175,7 @@ Annotate nodes with their distance from the current node:
 
 Transitive reduction removes redundant edges. An edge A → C is redundant if C is already reachable from A through other paths (of length >= 2).
 
-Dry run — find redundant edges without removing them:
+Dry run - find redundant edges without removing them:
 
 ```python
 >>> NetworkNode.objects.transitive_reduction()

docs/transformations.md

@@ -14,14 +14,14 @@ This installs `networkx`, `rustworkx`, and `pandas`.
 
 ## Common parameters
 
-All export functions accept a queryset of either nodes or edges. The queryset type is detected automatically — pass a nodes queryset and the corresponding edges are looked up, or vice versa.
+All export functions accept a queryset of either nodes or edges. The queryset type is detected automatically - pass a nodes queryset and the corresponding edges are looked up, or vice versa.
 
 These parameters are shared across `nx_from_queryset`, `rx_from_queryset`, and `json_from_queryset`:
 
-- `node_attribute_fields_list` — field names to include as attributes on each node
-- `edge_attribute_fields_list` — field names to include as attributes on each edge
-- `date_strf` — strftime format string for date-like fields (e.g. `"%Y-%m-%d"`)
-- `digraph` — `True` for a directed graph, `False` (default) for undirected
+- `node_attribute_fields_list` - field names to include as attributes on each node
+- `edge_attribute_fields_list` - field names to include as attributes on each edge
+- `date_strf` - strftime format string for date-like fields (e.g. `"%Y-%m-%d"`)
+- `digraph` - `True` for a directed graph, `False` (default) for undirected
 
 ## NetworkX export
 
@@ -158,11 +158,11 @@ True
 
 The output follows the node-link JSON format with these top-level keys:
 
-- `directed` — whether the graph is directed
-- `multigraph` — whether the graph allows multiple edges
-- `attrs` — graph-level attributes
-- `nodes` — list of node objects, each with `id` (index) and `data` dict
-- `links` — list of edge objects, each with `source`, `target`, `id`, and `data` dict
+- `directed` - whether the graph is directed
+- `multigraph` - whether the graph allows multiple edges
+- `attrs` - graph-level attributes
+- `nodes` - list of node objects, each with `id` (index) and `data` dict
+- `links` - list of edge objects, each with `source`, `target`, `id`, and `data` dict
 
 By default, `digraph=False` produces undirected output:
 
@@ -202,7 +202,7 @@ These functions use NetworkX's Weisfeiler-Lehman algorithm to compute structural
 True
 ```
 
-WL hashing is not collision-free — hash equality is necessary but not sufficient for true isomorphism.
+WL hashing is not collision-free - hash equality is necessary but not sufficient for true isomorphism.
 
 These functions also have node-level convenience methods that use lazy imports (so NetworkX isn't required at import time):
 
@@ -217,8 +217,8 @@ Scope options: `"connected"`, `"descendants"`, `"ancestors"`, `"clan"`.
 
 These functions are used internally by the export functions, but can be useful when working with querysets directly.
 
-**`edges_from_nodes_queryset(nodes_queryset)`** — Given a queryset of nodes, returns a queryset of all edges where both parent and child are in the provided nodes.
+**`edges_from_nodes_queryset(nodes_queryset)`** - Given a queryset of nodes, returns a queryset of all edges where both parent and child are in the provided nodes.
 
-**`nodes_from_edges_queryset(edges_queryset)`** — Given a queryset of edges, returns a queryset of all nodes that appear as a parent or child in the provided edges.
+**`nodes_from_edges_queryset(edges_queryset)`** - Given a queryset of edges, returns a queryset of all nodes that appear as a parent or child in the provided edges.
 
-**`model_to_dict(instance, fields=None, date_strf=None)`** — Converts a model instance to a dictionary for the specified fields. Handles ForeignKeys, ManyToMany fields (with `__` subfield lookup), dates, UUIDs, file fields, and callable methods.
+**`model_to_dict(instance, fields=None, date_strf=None)`** - Converts a model instance to a dictionary for the specified fields. Handles ForeignKeys, ManyToMany fields (with `__` subfield lookup), dates, UUIDs, file fields, and callable methods.