update docs

docs/source/algorithms.rst

@@ -14,7 +14,7 @@ Utilities
 
 .. function:: Constant(data: [...])
 
-    Returns a constant relation containing the data passed in. The constant rule ``?[] <- ...`` is
+    Returns a relation containing the data passed in. The constant rule ``?[] <- ...`` is
     syntax sugar for ``?[] <~ Constant(data: ...)``.
 
     :param data: A list of lists, representing the rows of the returned relation.
@@ -31,9 +31,9 @@ Utilities
     :param take: How many rows at most to produce. Zero means no limit. Defaults to zero.
     :return: The returned relation, in addition to the rows specified in the parameter ``out``, will have the ordering prepended. The ordering starts at ``1``.
 
-.. TIP::
+    .. TIP::
 
-    This algorithm serves a similar purpose to the global ``:order``, ``:limit`` and ``:offset`` options, but can be applied to intermediate results. Prefer the global options if it is applied to the final output.
+        This algorithm serves a similar purpose to the global ``:order``, ``:limit`` and ``:offset`` options, but can be applied to intermediate results. Prefer the global options if it is applied to the final output.
 
 .. function:: CsvReader(url: ..., types: [...], delimiter: ',', prepend_index: false, has_headers: true)
 
@@ -135,10 +135,6 @@ Pathfinding algorithms
     :param limit: How many answers to produce for each starting nodes. Defaults to 1.
     :return: Triples containing the starting node, the answer node, and the found path connecting them.
 
-.. TIP::
-
-    You probably don't want to use depth first search for path finding unless you have a really niche use case.
-
 .. function:: DFS(...)
 
     See :func:`Algo.DepthFirstSearch`.
@@ -153,13 +149,13 @@ Pathfinding algorithms
 
     :return: 4-tuples containing the starting node index, the goal node index, the lowest cost, and a path with the lowest cost.
 
-.. TIP::
+    .. TIP::
 
-    The performance of A\* star algorithm heavily depends on how good your heuristic function is. Passing in ``0`` as the estimate is always valid, but then you really should be using Dijkstra's algorithm.
+        The performance of A\* star algorithm heavily depends on how good your heuristic function is. Passing in ``0`` as the estimate is always valid, but then you really should be using Dijkstra's algorithm.
 
-    Good heuristics usually come about from a metric in the ambient space in which your data live, e.g. spherical distance on the surface of a sphere, or Manhattan distance on a grid. :func:`Func.Math.haversine_deg_input` could be helpful for the spherical case. Note that you must use the correct units for the distance.
+        Good heuristics usually come about from a metric in the ambient space in which your data live, e.g. spherical distance on the surface of a sphere, or Manhattan distance on a grid. :func:`Func.Math.haversine_deg_input` could be helpful for the spherical case. Note that you must use the correct units for the distance.
 
-    Providing a heuristic that is not guaranteed to be a lower-bound *might* be acceptable if you are fine with inaccuracies. The errors in the answers are bound by the sum of the margins of your over-estimates.
+        Providing a heuristic that is not guaranteed to be a lower-bound *might* be acceptable if you are fine with inaccuracies. The errors in the answers are bound by the sum of the margins of your over-estimates.
 
 -------------------------------------
 Community detection algorithms
@@ -224,9 +220,9 @@ Centrality measures
     :param undirected: Whether the edges should be interpreted as undirected. Defaults to ``false``.
     :return: Node index together with its centrality.
 
-.. WARNING::
+    .. WARNING::
 
-    ``BetweennessCentrality`` is very expensive to compute for medium to large graphs. Plan resources accordingly.
+        ``BetweennessCentrality`` is very expensive for medium to large graphs. If possible, collapse large graphs into supergraphs by running a community detection algorithm first.
 
 ------------------
 Miscellaneous

docs/source/setup.rst

@@ -146,8 +146,26 @@ A few other magic commands are available:
 * ``%cozo_clear`` clears all set parameters.
 * ``%cozo_params`` returns the parameters currently set.
 
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Python
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can also use the Python client without JupyterLab. First::
+
+    pip install pycozo
+
+Next, in your Python code, do something like the following::
+
+    from pycozo.client import Client
+
+    client = Client(host='http://127.0.0.1:9070', auth=None, dataframe=False)
+    print(client.run('::relations'))
+
+If ``dataframe=True``, the client will transform the returned relation into Pandas dataframes, which must be separately installed.
+The ``client.run`` method also takes an optional second argument ``params``.
+
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-The Makeshift JavaScript Console
+Web Browser
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 If you are reluctant to install python and Jupyter, you may consider the Makeshift JavaScript Console.

docs/source/sysops.rst

@@ -48,7 +48,7 @@ Ops for stored relations
 
     Set triggers for the stored relation ``<REL_NAME>``. This is explained in more detail in the transaction chapter.
 
-.. function:: ::access_level <REL_NAME> <ACCESS_LEVEL>
+.. function:: ::access_level <ACCESS_LEVEL> <REL_NAME> (, <REL_NAME>)*
 
     Sets the access level of ``<REL_NAME>`` to the given level. The levels are: