3. Plotting and Debugging

Plotting

For Errors & debugging it is necessary to visualize the graph-operation. You may plot the original plot and annotate on top the execution plan and solution of the last computation, calling methods with arguments like this:

graphop.plot(show=True)                # open a matplotlib window
graphop.plot("graphop.svg")              # other supported formats: png, jpg, pdf, ...
graphop.plot()                         # without arguments return a pydot.DOT object
graphop.plot(solution=out)             # annotate graph with solution values

The legend for all graphtik diagrams, generated by graphtik.plot.legend().

The same plot() methods are defined on a NetworkOperation, Network & ExecutionPlan, each one capable to produce diagrams with increasing complexity. Whenever possible, the top-level plot() methods delegates to the ones below.

For instance, when a net-operation has just been composed, plotting it will come out bare bone, with just the 2 types of nodes (data & operations), their dependencies, and the sequence of the execution-plan.

But as soon as you run it, the net plot calls will print more of the internals. Internally it delegates to ExecutionPlan.plot() of ``graph_op.last_plan`() attribute, which caches the last run to facilitate debugging. If you want the bare-bone diagram, plot network:

netop.net.plot(...)

Note

For plots, Graphviz program must be in your PATH, and pydot & matplotlib python packages installed. You may install both when installing graphtik with its plot extras:

pip install graphtik[plot]

Tip

The pydot.Dot instances returned by plot() are rendered directly in Jupyter/IPython notebooks as SVG images.

Errors & debugging

Graphs may become arbitrary deep. Launching a debugger-session to inspect deeply nested stacks is notoriously hard

As a workaround, when some operation fails, the original exception gets annotated with the folllowing properties, as a debug aid:

>>> from graphtik import compose, operation
>>> from pprint import pprint

>>> def scream(*args):
...     raise ValueError("Wrong!")

>>> try:
...     compose("errgraph")(
...        operation(name="screamer", needs=['a'], provides=["foo"])(scream)
...     )({'a': None})
... except ValueError as ex:
...     pprint(ex.jetsam)
{'args': {'args': [None], 'kwargs': {}},
 'executed': set(),
 'network': Network(
    +--a
    +--FunctionalOperation(name='screamer', needs=['a'], provides=['foo'], fn='scream')
    +--foo),
 'operation': FunctionalOperation(name='screamer', needs=['a'], provides=['foo'], fn='scream'),
 'outputs': None,
 'plan': ExecutionPlan(inputs=('a',), outputs=(), steps:
  +--FunctionalOperation(name='screamer', needs=['a'], provides=['foo'], fn='scream')),
 'provides': ['foo'],
 'results': None,
 'solution': {'a': None}}

In interactive REPL console you may use this to get the last raised exception:

import sys

sys.last_value.jetsam

The following annotated attributes might have meaningfull value on an exception:

network

the innermost network owning the failed operation/function

plan

the innermost plan that executing when a operation crashed

operation

the innermost operation that failed

args

either the input arguments list fed into the function, or a dict with both args & kwargs keys in it.

outputs

the names of the outputs the function was expected to return

provides

the names eventually the graph needed from the operation; a subset of the above, and not always what has been declared in the operation.

results

the values dict, if any; it maybe a zip of the provides with the actual returned values of the function, ot the raw results.

executed`

a set with the operation nodes & instructions executed till the error happened.

Ofcourse you may use many of the above “jetsam” values when plotting.

Note

The Plotting capabilities, along with the above annotation of exceptions with the internal state of plan/operation often renders a debugger session unnecessary. But since the state of the annotated values might be incomple, you may not always avoid one.

Execution internals