A simple calculator

    >>> from cogent.recalculation.definition import *
    >>> def add(*args):
    ...     return sum(args)
    ...
    >>> top = CalcDefn(add)(ParamDefn('A'), ParamDefn('B'))
    >>> pc = top.makeParamController()
    >>> f = pc.makeCalculator()

f.getValueArray() shows the inputs, ie: the optimisable parameters

    >>> f.getValueArray()
    [1.0, 1.0]

The calculator can be called like a function

    >>> f([3.0, 4.25])
    7.25

Or just a subset of the inputs can be changed directly

    >>> f.change([(1, 4.5)])
    7.5
    >>> f.getValueArray()
    [3.0, 4.5]

Now with scopes.  We will set up the calculation
      result = (Ax+Bx) + (Ay+By) + (Az+Bz)

A and B will remain distinct parameters, but x,y and z are merely scopes - ie:
it may be the case that Ax = Ay = Az, and that may simplify the calculation, but
we will never even notice if Ax = Bx.
Each scope dimension (here there is just one, 'category') must be collapsed away
at some point towards the end of the calculation if the calculation is to produce
a scalar result.  Here this is done with the selectFromDimension method.

    >>> A = ParamDefn('A', dimensions = ['category'])
    >>> B = ParamDefn('B', dimensions = ['category'])
    >>> mid = CalcDefn(add, name="mid")(A, B)
    >>> top = CalcDefn(add)(
    ...     mid.selectFromDimension('category', "x"),
    ...     mid.selectFromDimension('category', "y"),
    ...     mid.selectFromDimension('category', "z"))
    ...
    >>> # or equivalently:
    >>> # top = CalcDefn(add, *mid.acrossDimension('category',
    >>> #     ['x', 'y', 'z']))
    >>>
    >>> pc = top.makeParamController()
    >>> f = pc.makeCalculator()
    >>> f.getValueArray()
    [1.0, 1.0]

There are still only 2 inputs because the default scope
is global, ie: Ax == Ay == Az.  If we allow A to be
different in the x,y and z categories and set their
initial values to 2.0:

    >>> pc.assignAll('A', value=2.0, independent=True)
    >>> f = pc.makeCalculator()
    >>> f.getValueArray()
    [1.0, 2.0, 2.0, 2.0]

Now we have A local and B still global, so the calculation is
(Ax+B) + (Ay+B) + (Az+B) with the input parameters being
[B, Ax, Ay, Az], so:

    >>> f([1.0, 2.0, 2.0, 2.0])
    9.0
    >>> f([0.25, 2.0, 2.0, 2.0])
    6.75

Constants do not appear in the optimisable inputs.
Set one of the 3 A values to be a constant and there
will be one fewer optimisable parameters:

    >>> pc.assignAll('A', scope_spec={'category':'z'}, const=True)
    >>> f = pc.makeCalculator()
    >>> f.getValueArray()
    [1.0, 2.0, 2.0]

The parameter controller should catch cases where the specified scope
does not exist:

   >>> pc.assignAll('A', scope_spec={'category':'nosuch'})
   Traceback (most recent call last):
   InvalidScopeError: ...
   >>> pc.assignAll('A', scope_spec={'nonsuch':'nosuch'})
   Traceback (most recent call last):
   InvalidDimensionError: ...

It is complicated guesswork matching the parameters you expect with positions in
the value array, let alone remembering whether or not they are presented to the
optimiser as logs, so .getValueArray(), .change() and .__call__() should only be
used by optimisers.  For other purposes there is an alternative, human friendly
interface:
    
    >>> pc.updateFromCalculator(f)
    >>> pc.getParamValue('A', category='x')
    2.0
    >>> pc.getParamValue('B', category=['x', 'y'])
    1.0

Despite the name, .getParamValue can get the value from any step in the
calculation, so long as it has a unique name.

    >>> pc.getParamValue('mid', category='x')
    3.0

For bulk retrieval of parameter values by parameter name and scope name there is
the .getParamValueDict() method:

    >>> pc.getParamValueDict(['category']).keys()
    ['A', 'B']
    >>> pc.getParamValueDict(['category'])['A']['x']
    2.0

Here is a function that is more like a likelihood function, in that it has a
maximum:

    >>> def curve(x, y):
    ...     return 0 - (x**2 + y**2)
    ...
    >>> top = CalcDefn(curve)(ParamDefn('X'), ParamDefn('Y'))
    >>> pc = top.makeParamController()
    >>> f = pc.makeCalculator()

Now ask it to find the maximum.  It is a simple function with only one local
maximum so local optimisation should be enough:
    
    >>> f.optimise(local=True)
    >>> pc.updateFromCalculator(f)

There were two parameters, X and Y, and at the maximum they should both be 0.0:
    
    >>> pc.getParamValue('Y')
    0.0
    >>> pc.getParamValue('X')
    0.0

Because this function has a maximum it is possible to ask it for a confidence
interval around a parameter, ie: how far from 0.0 can we move x before f(x,y)
falls bellow f(X,Y)-dropoff:
    
    >>> pc.getParamInterval('X', dropoff=4, xtol=0.0)
    (-2.0, 0.0, 2.0)

We test the ability to omit xtol. Due to precision issues we convert the returned value to a string.

    >>> '-2.0, 0.0, 2.0' == "%.1f, %.1f, %.1f" % pc.getParamInterval('X', dropoff=4)
    True

And finally intervals can be calculated in bulk by passing a dropoff value to
.getParamValueDict():
    
    >>> pc.getParamValueDict([], dropoff=4, xtol=0.0)['X']
    (-2.0, 0.0, 2.0)

For likelihood functions it is more convenient to provide 'p' rather than 'dropoff', dropoff = chdtri(1, p) / 2.0.  Also in general you won't need ultra precise answers, so don't use 'xtol=0.0', that's just to make the doctest work.


