At a high level, an operation
is a function in a computation pipeline
, abstractly represented by the .Operation
class. This class specifies the dependencies <dependency>
of the operation in the pipeline.
You may inherit this class and access the declared values in needs
from solution
and produce the declared provides
when Operation.compute()
method is called. But there is an easier way...actually half of the code of this project is to retrofit existing functions into operations.
The .FunctionalOperation
provides a concrete lightweight wrapper around any arbitrary function to define those dependencies. Instead of constructing it directly, prefer to instantiate it by calling the .operation()
factory:
>>> from operator import add >>> from graphtik import operation
>>> add_op = operation(add, ... needs=['a', 'b'], ... provides=['a_plus_b']) >>> add_op FunctionalOperation(name='add', needs=['a', 'b'], provides=['a_plus_b'], fn='add')
You may still call the original function, by accessing the .FunctionalOperation.fn
attribute:
>>> add_op.fn(3, 4) == add(3, 4) True
Calling an operation, it delegates to
.Operation.compute()
method, which checks the inputs, match the needs/provides to function arguments, calls the function, and finally "zip" the function results with the operation's provides. (read more ongraph-computations
).
There are two ways to instantiate a .FunctionalOperation
s, each one suitable for different scenarios.
We've seen that calling manually .operation()
allows putting into a pipeline functions that are defined elsewhere (e.g. in another module, or are system functions).
But that method is also useful if you want to create multiple operation instances with similar attributes, e.g. needs
:
>>> op_factory = operation(needs=['a'])
Notice that we specified a fn, in order to get back a .FunctionalOperation
instance (and not a decorator).
>>> from graphtik import operation, compose >>> from functools import partial
>>> def mypow(a, p=2): ... return a ** p
>>> pow_op2 = op_factory.withset(fn=mypow, provides="^2") >>> pow_op3 = op_factory.withset(fn=partial(mypow, p=3), name='pow_3', provides='^3') >>> pow_op0 = op_factory.withset(fn=lambda a: 1, name='pow_0', provides='^0')
>>> graphop = compose('powers', pow_op2, pow_op3, pow_op0) >>> graphop Pipeline('powers', needs=['a'], provides=['^2', '^3', '^0'], x3 ops: mypow, pow_3, pow_0)
>>> graphop(a=2) {'a': 2, '^2': 4, '^3': 8, '^0': 1}
Tip
See plotting
on how to make diagrams like this.
If you are defining your computation graph and the functions that comprise it all in the same script, the decorator specification of operation
instances might be particularly useful, as it allows you to assign computation graph structure to functions as they are defined. Here's an example:
>>> from graphtik import operation, compose
>>> @operation(needs=['b', 'a', 'r'], provides='bar') ... def foo(a, b, c): ... return c * (a + b)
>>> graphop = compose('foo_graph', foo)
- Notice that if
name
is not given, it is deduced from the function name.
Each operation
is a node in a computation graph
, depending and supplying data from and to other nodes (via the solution
), in order to compute
.
This graph structure is specified (mostly) via the provides
and needs
arguments to the .operation
factory, specifically:
needs
this argument names the list of (positionally ordered)
inputs
data the operation requires to receive from solution. The list corresponds, roughly, to the arguments of the underlying function (plus anysideffects
).It can be a single string, in which case a 1-element iterable is assumed.
- seealso
needs
,modifier
,.FunctionalOperation.needs
,.FunctionalOperation.op_needs
,.FunctionalOperation._fn_needs
provides
this argument names the list of (positionally ordered)
outputs
data the operation provides into the solution. The list corresponds, roughly, to the returned values of the fn (plus anysideffects
&alias
es).It can be a single string, in which case a 1-element iterable is assumed.
If they are more than one, the underlying function must return an iterable with same number of elements (unless it
returns dictionary
).- seealso
provides
,modifier
,.FunctionalOperation.provides
,.FunctionalOperation.op_provides
,.FunctionalOperation._fn_provides
Declarations of needs and provides is affected by modifier
s like .mapped
:
graphtik.modifiers.mapped
graphtik.modifiers.optional
graphtik.modifiers.vararg
graphtik.modifiers.varargs
Sometimes, you need to interface functions & operations where they name a dependency
differently. This is doable without introducing "pipe-through" interface operation, either by annotating certain needs with .mapped
modifiers (above), or by alias
sing certain provides to different names:
>>> op = operation(str, ... name="provides with aliases", ... needs="anything", ... provides="real thing", ... aliases=("real thing", "phony"))
When many operations are composed into a computation graph, Graphtik matches up the values in their needs and provides to form the edges of that graph (see graph-composition
for more on that), like the operations from the script in quick-start
:
>>> from operator import mul, sub >>> from functools import partial >>> from graphtik import compose, operation
>>> def abspow(a, p): ... """Compute ^p. """ ... c = abs(a) ** p ... return c
>>> # Compose the mul, sub, and abspow operations into a computation graph. >>> graphop = compose("graphop", ... operation(mul, needs=["a", "b"], provides=["ab"]), ... operation(sub, needs=["a", "ab"], provides=["a_minus_ab"]), ... operation(name="abspow1", needs=["a_minus_ab"], provides=["abs_a_minus_ab_cubed"]) ... (partial(abspow, p=3)) ... ) >>> graphop Pipeline('graphop', needs=['a', 'b', 'ab', 'a_minus_ab'], provides=['ab', 'a_minus_ab', 'abs_a_minus_ab_cubed'], x3 ops: mul, sub, abspow1)
- Notice the use of
functools.partial()
to set parameterp
to a constant value. - And this is done by calling once more the returned "decorator* from
operation()
, when called without a functions.
The needs
and provides
arguments to the operations in this script define a computation graph that looks like this: