- Author:
- David Nickerson <david.nickerson@gmail.com>
- Date:
- 2021-09-16 00:41:19+12:00
- Desc:
- Updating Noble 1962 model:
* Exposing the membrane potential to the top-level model;
* adding SED-ML for the paced and pacemaker variants of the model.
Using OpenCOR Snapshot release 2021-09-14.
- Permanent Source URI:
- https://models.fieldml.org/workspace/a1/rawfile/f954e59183314cd37f86c8832dc81317d01c8ec5/dojo-presentation/js/dojo/dojox/lang/functional/tailrec.js
dojo.provide("dojox.lang.functional.tailrec");
dojo.require("dojox.lang.functional.lambda");
dojo.require("dojox.lang.functional.util");
// This module provides recursion combinators:
// - a tail recursion combinator.
// Acknoledgements:
// - recursion combinators are inspired by Manfred von Thun's article
// "Recursion Theory and Joy"
// (http://www.latrobe.edu.au/philosophy/phimvt/joy/j05cmp.html)
// Notes:
// - recursion combinators produce a function, which implements
// their respective recusion patterns. String lambdas are inlined, if possible.
(function(){
var df = dojox.lang.functional, inline = df.inlineLambda, _x ="_x";
df.tailrec = function(
/*Function|String|Array*/ cond,
/*Function|String|Array*/ then,
/*Function|String|Array*/ before){
// summary:
// Generates a function for the tail recursion pattern. This is the simplified
// version of the linear recursive combinator without the "after" function,
// and with the modified "before" function. All parameter functions are called
// in the context of "this" object.
// cond:
// The lambda expression, which is used to detect the termination of recursion.
// It accepts the same parameter as the generated recursive function itself.
// This function should return "true", if the recursion should be stopped,
// and the "then" part should be executed. Otherwise the recursion will proceed.
// then:
// The lambda expression, which is called upon termination of the recursion.
// It accepts the same parameters as the generated recursive function itself.
// The returned value will be returned as the value of the generated function.
// before:
// The lambda expression, which is called before the recursive step.
// It accepts the same parameter as the generated recursive function itself,
// and returns an array of arguments for the next recursive call of
// the generated function.
var c, t, b, cs, ts, bs, dict1 = {}, dict2 = {},
add2dict = function(x){ dict1[x] = 1; };
if(typeof cond == "string"){
cs = inline(cond, _x, add2dict);
}else{
c = df.lambda(cond);
cs = "_c.apply(this, _x)";
dict2["_c=_t.c"] = 1;
}
if(typeof then == "string"){
ts = inline(then, _x, add2dict);
}else{
t = df.lambda(then);
ts = "_t.t.apply(this, _x)";
}
if(typeof before == "string"){
bs = inline(before, _x, add2dict);
}else{
b = df.lambda(before);
bs = "_b.apply(this, _x)";
dict2["_b=_t.b"] = 1;
}
var locals1 = df.keys(dict1), locals2 = df.keys(dict2),
f = new Function([], "var _x=arguments,_t=_x.callee,_c=_t.c,_b=_t.b".concat( // Function
locals1.length ? "," + locals1.join(",") : "",
locals2.length ? ",_t=_x.callee," + locals2.join(",") : t ? ",_t=_x.callee" : "",
";for(;!",
cs,
";_x=",
bs,
");return ",
ts
));
if(c){ f.c = c; }
if(t){ f.t = t; }
if(b){ f.b = b; }
return f;
};
})();
/*
For documentation only:
1) The original recursive version:
var tailrec1 = function(cond, then, before){
var cond = df.lambda(cond),
then = df.lambda(then),
before = df.lambda(before);
return function(){
if(cond.apply(this, arguments)){
return then.apply(this, arguments);
}
var args = before.apply(this, arguments);
return arguments.callee.apply(this, args);
};
};
2) The original iterative version (before minification and inlining):
var tailrec2 = function(cond, then, before){
var cond = df.lambda(cond),
then = df.lambda(then),
before = df.lambda(before);
return function(){
var args = arguments;
for(; !cond.apply(this, args); args = before.apply(this, args));
return then.apply(this, args);
};
};
*/
