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v23 of FARGish: Restarting from scratch, this time in Python

v22/ contains the last attempt in Racket, abandoned. Also includes old
Clojure code from still earlier versions.


To run with a graphical display in a web browser:

1. In a terminal window, run:

   $ python3.7 server.py

2. In a web browser, open:

   http://localhost:8081/fargish.html

To stop the web server, hit Ctrl-C in the terminal window.

11-Oct-2019: First running version, still with bugs and missing features.

5-Dec-2019
  Now in the middle of rewriting the do_timestep and Response code.
  The new way has a mix-in object, TimeStepper, which will replace
  NumboGraph.do_timestep() and its ancillary functions. Watcher is now
  ActiveNode and Response is now Action. The new files are:
     bases.py             ActiveNode and Action
     TimeStepper.py
     testTimeStepper.py   Has simple example of setting up a tiny FARG model

  numbo3.py is the current version.

18-Feb-2020
  demo2.py is the current version.

========================================================================

The Design of FARGish
(last updated 24-Feb-2020)

Under FARGish, a FARG model is a port graph plus program code.

PortGraph (inherits from MultiGraph from the networkx library): holds the
graph. Every edge in a PortGraph links a (nodeid, port label) tuple to another
(nodeid, port label) tuple. Each nodeid is an integer > 1. Each port label is a
string.

With every node in the PortGraph is associated an object, called a datum.
Every datum inherits from Node.

ActiveNode is an abstract class that inherits from Node, and provides an
.actions() method, which returns a list of Action objects. An Action object's
.go() method performs some action on the graph. An Action object has a .weight
and a .threshold, which determine how likely the Action is to be executed on
a given timestep. An Action is not eligible to be executed unless its .weight
exceeds its .threshold. (Purpose: a nonzero threshold prevents highly
consequential Actions from being executed before their ActiveNode has a high
level of support from other nodes.)

Every node has salience and support. Salience is a number that determines how
likely a node is to be found in searches. A node's salience is boosted when it
or its immediate neighbors is "touched", i.e. affected by an Action. Each
node's salience decays at the start of each timestep. Support is a number that
determines whether a node can continue to exist and, for ActiveNodes, how
likely they are to be chosen to perform one of their Actions. Support flows
from node to node via "support edges": those with labels 'support_to' and
'support_from'. The exact way in which support flows is determined by a
Propagator object.

TimeStepper is a mix-in class for PortGraph, which provides a .do_timestep()
method. .do_timestep() randomly chooses ActiveNodes (based on support), and
from those ActiveNodes randomly chooses Actions.

The FARGish language provides simple syntax for defining subclasses of Node,
which port labels mate with which other port labels, and simple Actions
associated with ActiveNodes and simple conditions for executing them. For
anything complicated, FARGish provides the ability to call to external Python
code.

FARGish is compiled into Python by functions in codegen.py. The grammar,
implemented in PLY, is specified in grammar.py. grammar.parse() generates
objects defined in gen.py, which in turn have a .gen() method to generate
Python code.

Visualization of the graph occurs in a web browser and is implemented in
Javascript in the file forcedirected3.js, invoked by fargish.html. server.py
implements the web server, calling upon runner.py to interpret HTTP GET
commands to affect or query the FARG model. runner.py calls upon ModelWrapper
to directly access the model and to convert between the model's native Python
objects and the JSON objects sent to the web browser.

@@source files actually in use