5. API Reference¶

`graphtik`	Lightweight computation graphs for Python.
`graphtik.op`	About operation nodes (but not net-ops to break cycle).
`graphtik.netop`	About network operations (those based on graphs)
`graphtik.network`	Compile & execute network graphs of operations.
`graphtik.plot`
`graphtik.base`	Generic or specific utilities

Module: op¶

About operation nodes (but not net-ops to break cycle).

class graphtik.op.FunctionalOperation[source]¶

An operation performing a callable (ie a function, a method, a lambda).

Tip

Use operation() builder class to build instances of this class instead.

compute(named_inputs, outputs=None) → dict[source]¶

Compute (optional) asked outputs for the given named_inputs.

It is called by Network. End-users should simply call the operation with named_inputs as kwargs.

Parameters:	named_inputs (list) – the input values with which to feed the computation.
Returns list:	Should return a list values representing the results of running the feed-forward computation on `inputs`.

withset(**kw) → graphtik.op.FunctionalOperation[source]¶: Make a clone with the some values replaced.

Attention

Using namedtuple._replace() would not pass through cstor, so would not get a nested name with parents, not arguments validation.

class graphtik.op.Operation[source]¶

An abstract class representing an action with compute().

compute(named_inputs, outputs=None)[source]¶

Compute (optional) asked outputs for the given named_inputs.

It is called by Network. End-users should simply call the operation with named_inputs as kwargs.

Parameters:	named_inputs (list) – the input values with which to feed the computation.
Returns list:	Should return a list values representing the results of running the feed-forward computation on `inputs`.

class graphtik.op.operation(fn: Callable = None, *, name=None, needs: Union[Collection[T_co], str, None] = None, provides: Union[Collection[T_co], str, None] = None, returns_dict=None, node_props: Mapping[KT, VT_co] = None)[source]¶

A builder for graph-operations wrapping functions.

Parameters:

fn (function) – The function used by this operation. This does not need to be specified when the operation object is instantiated and can instead be set via __call__ later.
name (str) – The name of the operation in the computation graph.
needs (list) – Names of input data objects this operation requires. These should correspond to the args of fn.
provides (list) – Names of output data objects this operation provides. If more than one given, those must be returned in an iterable, unless returns_dict is true, in which cae a dictionary with as many elements must be returned
returns_dict (bool) – if true, it means the fn returns a dictionary with all provides, and no further processing is done on them (i.e. the returned output-values are not zipped with provides)
node_props – added as-is into NetworkX graph

Returns:

when called, it returns a FunctionalOperation

Example:

This is an example of its use, based on the “builder pattern”:

>>> from graphtik import operation

>>> opb = operation(name='add_op')
>>> opb.withset(needs=['a', 'b'])
operation(name='add_op', needs=['a', 'b'], provides=[], fn=None)
>>> opb.withset(provides='SUM', fn=sum)
operation(name='add_op', needs=['a', 'b'], provides=['SUM'], fn='sum')

You may keep calling withset() till you invoke a final __call__() on the builder; then you get the actual FunctionalOperation instance:

>>> # Create `Operation` and overwrite function at the last moment.
>>> opb(sum)
FunctionalOperation(name='add_op', needs=['a', 'b'], provides=['SUM'], fn='sum')

Tip

Remember to call once more the builder class at the end, to get the actual operation instance.

withset(*, fn: Callable = None, name=None, needs: Union[Collection[T_co], str, None] = None, provides: Union[Collection[T_co], str, None] = None, returns_dict=None, node_props: Mapping[KT, VT_co] = None) → graphtik.op.operation[source]¶

graphtik.op.reparse_operation_data(name, needs, provides)[source]¶

Validate & reparse operation data as lists.

As a separate function to be reused by client code when building operations and detect errors aearly.

Module: netop¶

About network operations (those based on graphs)

class graphtik.netop.NetworkOperation(net, name, *, inputs=None, outputs=None, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None, method=None)[source]¶

An operation that can compute a network-graph of operations.

Tip

Use compose() factory to prepare the net and build instances of this class.

compute(named_inputs, outputs=None) → graphtik.network.Solution[source]¶

Solve & execute the graph, sequentially or parallel.

It see also Operation.compute().

Parameters:

named_inputs (dict) – A maping of names –> values that must contain at least the compulsory inputs that were specified when the plan was built (but cannot enforce that!). Cloned, not modified.
outputs – a string or a list of strings with all data asked to compute. If you set this variable to None, all data nodes will be kept and returned at runtime.

Returns:

The solution which contains the results of each operation executed +1 for inputs in separate dictionaries.

Raises:

ValueError –

If outputs asked do not exist in network, with msg:

Unknown output nodes: …
If plan does not contain any operations, with msg:

Unsolvable graph: …
If given inputs mismatched plan’s needs, with msg:

Plan needs more inputs…
If outputs asked cannot be produced by the dag, with msg:

Impossible outputs…

inputs = None[source]¶: The inputs names (possibly None) used to compile the plan.

last_plan = None[source]¶: The execution_plan of the last call to compute(), stored as debugging aid.

method = None[source]¶: set execution mode to single-threaded sequential by default

narrowed(inputs: Union[Collection[T_co], str, None] = None, outputs: Union[Collection[T_co], str, None] = None, name=None, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None) → graphtik.netop.NetworkOperation[source]¶

Return a copy with a network pruned for the given needs & provides.

Parameters:

inputs – prune net against these possbile inputs for compute(); method will WARN for any irrelevant inputs given. If None, they are collected from the net. They become the needs of the returned netop.
outputs – prune net against these possible outputs for compute(); method will RAISE if any irrelevant outputs asked. If None, they are collected from the net. They become the provides of the returned netop.
name –
the name for the new netop:
- if None, the same name is kept;
- if True, a distinct name is devised:
```
<old-name>-<uid>
```
- otherwise, the given name is applied.
predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

Returns:

A narrowed netop clone, which MIGHT be empty!*

Raises:

ValueError –

If outputs asked do not exist in network, with msg:

Unknown output nodes: …

outputs = None[source]¶: The outputs names (possibly None) used to compile the plan.

set_execution_method(method)[source]¶

Determine how the network will be executed.

Parameters:	method (str) – If “parallel”, execute graph operations concurrently using a threadpool.

graphtik.netop.compose(name, op1, *operations, needs: Union[Collection[T_co], str, None] = None, provides: Union[Collection[T_co], str, None] = None, merge=False, node_props=None, method=None) → graphtik.netop.NetworkOperation[source]¶

Composes a collection of operations into a single computation graph, obeying the merge property, if set in the constructor.

Parameters:	name (str) – A optional name for the graph being composed by this object. op1 – syntactically force at least 1 operation operations – Each argument should be an operation instance created using `operation`. merge (bool) – If `True`, this compose object will attempt to merge together `operation` instances that represent entire computation graphs. Specifically, if one of the `operation` instances passed to this `compose` object is itself a graph operation created by an earlier use of `compose` the sub-operations in that graph are compared against other operations passed to this `compose` instance (as well as the sub-operations of other graphs passed to this `compose` instance). If any two operations are the same (based on name), then that operation is computed only once, instead of multiple times (one for each time the operation appears). node_props – added as-is into NetworkX graph, to provide for filtering by `NetworkOperation.narrowed()`. method – either parallel or None (default); if `"parallel"`, launches multi-threading. Set when invoking a composed graph or by `NetworkOperation.set_execution_method()`.
Returns:	Returns a special type of operation class, which represents an entire computation graph as a single operation.
Raises:	ValueError – If the net` cannot produce the asked outputs from the given inputs.

Module: network¶

Compile & execute network graphs of operations.

exception graphtik.network.AbortedException[source]¶: Raised from the Network code when abort_run() is called.

graphtik.network.abort_run()[source]¶: Signal to the 1st running network to stop execution.

graphtik.network.is_abort()[source]¶: Return True if networks have been signaled to stop execution.

graphtik.network.is_skip_evictions()[source]¶: Return True if keeping all intermediate solution values, regardless of asked outputs.

graphtik.network.set_skip_evictions(skipped)[source]¶: If eviction is true, keep all intermediate solution values, regardless of asked outputs.

graphtik.network.set_endure_execution(endure)[source]¶: If endurance set to true, keep executing even of some operations fail.

graphtik.network.is_endure_execution()[source]¶: Is execution going even of some operations fail?

graphtik.network._execution_configs = <ContextVar name='execution_configs' default={'execution_pool': <multiprocessing.pool.ThreadPool object>, 'abort': False, 'skip_evictions': False, 'endure_execution': False}>[source]¶: Global configurations for all (nested) networks in a computaion run.

graphtik.network._unsatisfied_operations(dag, inputs: Collection[T_co]) → List[T][source]¶

Traverse topologically sorted dag to collect un-satisfied operations.

Unsatisfied operations are those suffering from ANY of the following:

They are missing at least one compulsory need-input.

Since the dag is ordered, as soon as we’re on an operation, all its needs have been accounted, so we can get its satisfaction.
Their provided outputs are not linked to any data in the dag.

An operation might not have any output link when _prune_graph() has broken them, due to given intermediate inputs.

Parameters:	dag – a graph with broken edges those arriving to existing inputs inputs – an iterable of the names of the input values
Returns:	a list of unsatisfied operations to prune

class graphtik.network.Network(*operations, graph=None)[source]¶

A graph of operations that can compile an execution plan.

Variables:	needs – the “base”, all data-nodes that are not produced by some operation provides – the “base”, all data-nodes produced by some operation

__abstractmethods__ = frozenset()[source]¶

__init__(*operations, graph=None)[source]¶

Parameters:	operations – to be added in the graph graph – if None, create a new.

__module__ = 'graphtik.network'[source]¶

__repr__()[source]¶: Return repr(self).

_abc_impl = <_abc_data object>[source]¶

_append_operation(graph, operation: graphtik.op.Operation)[source]¶

Adds the given operation and its data requirements to the network graph.

Invoked during constructor only (immutability).
Identities are based on the name of the operation, the names of the operation’s needs, and the names of the data it provides.

Parameters:	graph – the networkx graph to append to operation – operation instance to append

_apply_graph_predicate(graph, predicate)[source]¶

_build_execution_steps(pruned_dag, inputs: Collection[T_co], outputs: Optional[Collection[T_co]]) → List[T][source]¶

Create the list of operation-nodes & instructions evaluating all

operations & instructions needed a) to free memory and b) avoid overwritting given intermediate inputs.

Parameters:	pruned_dag – The original dag, pruned; not broken. outputs – outp-names to decide whether to add (and which) evict-instructions

Instances of _EvictInstructions are inserted in steps between operation nodes to reduce the memory footprint of solutions while the computation is running. An evict-instruction is inserted whenever a need is not used by any other operation further down the DAG.

_build_pydot(**kws)[source]¶

_cached_plans = None[source]¶: Speed up compile() call and avoid a multithreading issue(?) that is occuring when accessing the dag in networkx.

_prune_graph(inputs: Union[Collection[T_co], str, None], outputs: Union[Collection[T_co], str, None], predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None) → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7f7e26783208>, Collection[T_co], Collection[T_co], Collection[T_co]][source]¶

Determines what graph steps need to run to get to the requested outputs from the provided inputs: - Eliminate steps that are not on a path arriving to requested outputs; - Eliminate unsatisfied operations: partial inputs or no outputs needed; - consolidate the list of needs & provides.

Parameters:

inputs – The names of all given inputs.
outputs – The desired output names. This can also be None, in which case the necessary steps are all graph nodes that are reachable from the provided inputs.
predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

Returns:

a 3-tuple with the pruned_dag & the needs/provides resolved based on the given inputs/outputs (which might be a subset of all needs/outputs of the returned graph).

Use the returned needs/provides to build a new plan.

Raises:

ValueError –

if outputs asked do not exist in network, with msg:

Unknown output nodes: …

_topo_sort_nodes(dag) → List[T][source]¶: Topo-sort dag respecting operation-insertion order to break ties.

compile(inputs: Union[Collection[T_co], str, None] = None, outputs: Union[Collection[T_co], str, None] = None, predicate=None) → graphtik.network.ExecutionPlan[source]¶

Create or get from cache an execution-plan for the given inputs/outputs.

See _prune_graph() and _build_execution_steps() for detailed description.

Parameters:

inputs – A collection with the names of all the given inputs. If None`, all inputs that lead to given outputs are assumed. If string, it is converted to a single-element collection.
outputs – A collection or the name of the output name(s). If None`, all reachable nodes from the given inputs are assumed. If string, it is converted to a single-element collection.
predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.

Returns:

the cached or fresh new execution-plan

Raises:

ValueError –

If outputs asked do not exist in network, with msg:

Unknown output nodes: …
If solution does not contain any operations, with msg:

Unsolvable graph: …
If given inputs mismatched plan’s needs, with msg:

Plan needs more inputs…
If outputs asked cannot be produced by the dag, with msg:

Impossible outputs…

narrowed(inputs: Union[Collection[T_co], str, None] = None, outputs: Union[Collection[T_co], str, None] = None, predicate: Callable[[Any, Mapping[KT, VT_co]], bool] = None) → graphtik.network.Network[source]¶

Return a pruned network supporting just the given inputs & outputs.

Parameters:	inputs – all possible inputs names outputs – all possible output names predicate – the node predicate is a 2-argument callable(op, node-data) that should return true for nodes to include; if None, all nodes included.
Returns:	the pruned clone, or this, if both inputs & outputs were None

class graphtik.network.ExecutionPlan[source]¶

A pre-compiled list of operation steps that can execute for the given inputs/outputs.

It is the result of the network’s compilation phase.

Note the execution plan’s attributes are on purpose immutable tuples.

Variables:

net – The parent Network
needs – An iset with the input names needed to exist in order to produce all provides.
provides – An iset with the outputs names produces when all inputs are given.
dag – The regular (not broken) pruned subgraph of net-graph.
steps – The tuple of operation-nodes & instructions needed to evaluate the given inputs & asked outputs, free memory and avoid overwritting any given intermediate inputs.
evict – when false, keep all inputs & outputs, and skip prefect-evictions check.

__abstractmethods__ = frozenset()[source]¶

__dict__ = mappingproxy({'__module__': 'graphtik.network', '__doc__': "\n A pre-compiled list of operation steps that can :term:`execute` for the given inputs/outputs.\n\n It is the result of the network's :term:`compilation` phase.\n\n Note the execution plan's attributes are on purpose immutable tuples.\n\n :ivar net:\n The parent :class:`Network`\n :ivar needs:\n An :class:`iset` with the input names needed to exist in order to produce all `provides`.\n :ivar provides:\n An :class:`iset` with the outputs names produces when all `inputs` are given.\n :ivar dag:\n The regular (not broken) *pruned* subgraph of net-graph.\n :ivar steps:\n The tuple of operation-nodes & *instructions* needed to evaluate\n the given inputs & asked outputs, free memory and avoid overwritting\n any given intermediate inputs.\n :ivar evict:\n when false, keep all inputs & outputs, and skip prefect-evictions check.\n ", '_build_pydot': <function ExecutionPlan._build_pydot>, '__repr__': <function ExecutionPlan.__repr__>, 'validate': <function ExecutionPlan.validate>, '_check_if_aborted': <function ExecutionPlan._check_if_aborted>, '_call_operation': <function ExecutionPlan._call_operation>, '_execute_thread_pool_barrier_method': <function ExecutionPlan._execute_thread_pool_barrier_method>, '_execute_sequential_method': <function ExecutionPlan._execute_sequential_method>, 'execute': <function ExecutionPlan.execute>, '__dict__': <attribute '__dict__' of 'ExecutionPlan' objects>, '__abstractmethods__': frozenset(), '_abc_impl': <_abc_data object>})[source]¶

__module__ = 'graphtik.network'[source]¶

__repr__()[source]¶: Return a nicely formatted representation string

_abc_impl = <_abc_data object>[source]¶

_build_pydot(**kws)[source]¶

_call_operation(op, solution, endurance)[source]¶

_check_if_aborted(executed)[source]¶

_execute_sequential_method(solution: graphtik.network.Solution)[source]¶

This method runs the graph one operation at a time in a single thread

Parameters:	solution – must contain the input values only, gets modified

_execute_thread_pool_barrier_method(solution: graphtik.network.Solution)[source]¶

This method runs the graph using a parallel pool of thread executors. You may achieve lower total latency if your graph is sufficiently sub divided into operations using this method.

Parameters:	solution – must contain the input values only, gets modified

execute(named_inputs, outputs=None, *, method=None) → graphtik.network.Solution[source]¶

Parameters:

named_inputs – A maping of names –> values that must contain at least the compulsory inputs that were specified when the plan was built (but cannot enforce that!). Cloned, not modified.
outputs – If not None, they are just checked if possible, based on provides, and scream if not.

Returns:

The solution which contains the results of each operation executed +1 for inputs in separate dictionaries.

Raises:

ValueError –

If plan does not contain any operations, with msg:

Unsolvable graph: …
If given inputs mismatched plan’s needs, with msg:

Plan needs more inputs…
If outputs asked cannot be produced by the dag, with msg:

Impossible outputs…

validate(inputs: Union[Collection[T_co], str, None], outputs: Union[Collection[T_co], str, None])[source]¶

Scream on invalid inputs, outputs or no operations in graph.

Raises:	ValueError – If cannot produce any outputs from the given inputs, with msg: Unsolvable graph: … If given inputs mismatched plan’s `needs`, with msg: Plan needs more inputs… If outputs asked cannot be produced by the `dag`, with msg: Impossible outputs…

class graphtik.network.Solution(plan, *args, **kw)[source]¶

Collects outputs from operations, preserving overwrites.

Variables:

plan – the plan that produced this solution
executed –
A dictionary with keys the operations executed, and values their status:
- no key: not executed yet
- value None: execution ok
- value Exception: execution failed
passed – a “virtual” property with executed operations that had no exception
failures – a “virtual” property with executed operations that raised an exception
canceled – A sorted set of operations canceled due to upstream failures.
overwrites –
a “virtual” property to a dictionary with keys the names of values that exist more than once, and values, all those values in a list, ordered:
- before finsihed(), as computed;
- after finsihed(), in reverse.
finished – a flag denoting that this instance cannot acccept more results (after the finished() has been invoked)
times – a dictionary with execution timings for each operation

__init__(plan, *args, **kw)[source]¶: Initialize a ChainMap by setting maps to the given mappings. If no mappings are provided, a single empty dictionary is used.

__repr__()[source]¶: Return repr(self).

finish()[source]¶: invoked only once, after all ops have been executed

operation_executed(op, outputs)[source]¶: invoked once per operation, with its results

operation_failed(op, ex)[source]¶: invoked once per operation, with its results

overwrites[source]¶

The data in the solution that exist more than once.

Returns:	a dictionary with keys only those items that existed in more than one map, and values, all those values, in the order of given maps

Module: plot¶

Plotting of graphtik graphs.

graphtik.plot.build_pydot(graph, steps=None, inputs=None, outputs=None, solution=None, title=None, node_props=None, edge_props=None, clusters=None) → <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e241aa668>[source]¶

Build a Graphviz out of a Network graph/steps/inputs/outputs and return it.

See Plotter.plot() for the arguments, sample code, and the legend of the plots.

graphtik.plot.default_jupyter_render = {'svg_container_styles': '', 'svg_element_styles': 'width: 100%; height: 300px;', 'svg_pan_zoom_json': '{controlIconsEnabled: true, zoomScaleSensitivity: 0.4, fit: true}'}[source]¶

A nested dictionary controlling the rendering of graph-plots in Jupyter cells,

as those returned from Plotter.plot() (currently as SVGs). Either modify it in place, or pass another one in the respective methods.

The following keys are supported.

Parameters:

svg_pan_zoom_json –
arguments controlling the rendering of a zoomable SVG in Jupyter notebooks, as defined in https://github.com/ariutta/svg-pan-zoom#how-to-use if None, defaults to string (also maps supported):
```
"{controlIconsEnabled: true, zoomScaleSensitivity: 0.4, fit: true}"
```
svg_element_styles –
mostly for sizing the zoomable SVG in Jupyter notebooks. Inspect & experiment on the html page of the notebook with browser tools. if None, defaults to string (also maps supported):
```
"width: 100%; height: 300px;"
```
svg_container_styles – like svg_element_styles, if None, defaults to empty string (also maps supported).

graphtik.plot.legend(filename=None, show=None, jupyter_render: Mapping[KT, VT_co] = None, arch_url='https://graphtik.readthedocs.io/en/latest/arch.html')[source]¶

Generate a legend for all plots (see Plotter.plot() for args)

Parameters:	arch_url – the url to the architecture section explaining graphtik glassary.

See render_pydot() for the rest argyments.

graphtik.plot.render_pydot(dot: <sphinx.ext.autodoc.importer._MockObject object at 0x7f7e241aa7b8>, filename=None, show=False, jupyter_render: str = None)[source]¶

Plot a Graphviz dot in a matplotlib, in file or return it for Jupyter.

Parameters:

dot – the pre-built Graphviz pydot.Dot instance
filename (str) – Write diagram into a file. Common extensions are .png .dot .jpg .jpeg .pdf .svg call plot.supported_plot_formats() for more.
show – If it evaluates to true, opens the diagram in a matplotlib window. If it equals -1, it returns the image but does not open the Window.
jupyter_render –
a nested dictionary controlling the rendering of graph-plots in Jupyter cells. If None, defaults to default_jupyter_render (you may modify those in place and they will apply for all future calls).

You may increase the height of the SVG cell output with something like this:
```
netop.plot(jupyter_render={"svg_element_styles": "height: 600px; width: 100%"})
```

Returns:

the matplotlib image if show=-1, or the dot.

See Plotter.plot() for sample code.

graphtik.plot.supported_plot_formats() → List[str][source]¶: return automatically all pydot extensions

Module: base¶

Generic or specific utilities

class graphtik.base.Plotter[source]¶

Classes wishing to plot their graphs should inherit this and …

implement property plot to return a “partial” callable that somehow ends up calling plot.render_pydot() with the graph or any other args binded appropriately. The purpose is to avoid copying this function & documentation here around.

plot(filename=None, show=False, jupyter_render: Union[None, Mapping[KT, VT_co], str] = None, **kws)[source]¶

Entry-point for plotting ready made operation graphs.

Parameters:

filename (str) – Write diagram into a file. Common extensions are .png .dot .jpg .jpeg .pdf .svg call plot.supported_plot_formats() for more.
show – If it evaluates to true, opens the diagram in a matplotlib window. If it equals -1, it plots but does not open the Window.
inputs – an optional name list, any nodes in there are plotted as a “house”
outputs – an optional name list, any nodes in there are plotted as an “inverted-house”
solution – an optional dict with values to annotate nodes, drawn “filled” (currently content not shown, but node drawn as “filled”). It extracts more infos from a Solution instance, such as, if solution has an executed attribute, operations contained in it are drawn as “filled”.
title – an optional string to display at the bottom of the graph
node_props – an optional nested dict of Grapvhiz attributes for certain nodes
edge_props – an optional nested dict of Grapvhiz attributes for certain edges
clusters – an optional mapping of nodes –> cluster-names, to group them
jupyter_render – a nested dictionary controlling the rendering of graph-plots in Jupyter cells, if None, defaults to jupyter_render (you may modify it in place and apply for all future calls).

Returns:

a pydot.Dot instance (for for API reference visit: https://pydotplus.readthedocs.io/reference.html#pydotplus.graphviz.Dot)

Tip

The pydot.Dot instance returned is rendered directly in Jupyter/IPython notebooks as SVG images.

You may increase the height of the SVG cell output with something like this:

netop.plot(jupyter_render={"svg_element_styles": "height: 600px; width: 100%"})

Check default_jupyter_render for defaults.

Note that the graph argument is absent - Each Plotter provides its own graph internally; use directly render_pydot() to provide a different graph.

NODES:

oval: function
egg: subgraph operation
house: given input
inversed-house: asked output
polygon: given both as input & asked as output (what?)
square: intermediate data, neither given nor asked.
red frame: evict-instruction, to free up memory.
filled: data node has a value in solution OR function has been executed.
thick frame: function/data node in execution steps.

ARROWS

solid black arrows: dependencies (source-data need-ed by target-operations, sources-operations provides target-data)
dashed black arrows: optional needs
blue arrows: sideffect needs/provides
wheat arrows: broken dependency (provide) during pruning
green-dotted arrows: execution steps labeled in succession

To generate the legend, see legend().

Sample code:

>>> from graphtik import compose, operation
>>> from graphtik.modifiers import optional
>>> from operator import add

>>> netop = compose("netop",
...     operation(name="add", needs=["a", "b1"], provides=["ab1"])(add),
...     operation(name="sub", needs=["a", optional("b2")], provides=["ab2"])(lambda a, b=1: a-b),
...     operation(name="abb", needs=["ab1", "ab2"], provides=["asked"])(add),
... )

>>> netop.plot(show=True);                 # plot just the graph in a matplotlib window # doctest: +SKIP
>>> inputs = {'a': 1, 'b1': 2}
>>> solution = netop(**inputs)             # now plots will include the execution-plan

>>> netop.plot('plot1.svg', inputs=inputs, outputs=['asked', 'b1'], solution=solution);           # doctest: +SKIP
>>> dot = netop.plot(solution=solution);   # just get the `pydoit.Dot` object, renderable in Jupyter
>>> print(dot)
digraph G {
  fontname=italic;
  label=netop;
  a [fillcolor=wheat, shape=invhouse, style=filled, tooltip=1];
...

graphtik.base.aslist(i, argname, allowed_types=<class 'list'>)[source]¶: Utility to accept singular strings as lists, and None –> [].

graphtik.base.astuple(i, argname, allowed_types=<class 'tuple'>)[source]¶

graphtik.base.jetsam(ex, locs, *salvage_vars, annotation='jetsam', **salvage_mappings)[source]¶

Annotate exception with salvaged values from locals() and raise!

Parameters:

ex – the exception to annotate
locs –
locals() from the context-manager’s block containing vars to be salvaged in case of exception

ATTENTION: wrapped function must finally call locals(), because locals dictionary only reflects local-var changes after call.
annotation – the name of the attribute to attach on the exception
salvage_vars – local variable names to save as is in the salvaged annotations dictionary.
salvage_mappings – a mapping of destination-annotation-keys –> source-locals-keys; if a source is callable, the value to salvage is retrieved by calling value(locs). They take precendance over`salvae_vars`.

Raises:

any exception raised by the wrapped function, annotated with values assigned as atrributes on this context-manager

Any attrributes attached on this manager are attached as a new dict on the raised exception as new jetsam attrribute with a dict as value.
If the exception is already annotated, any new items are inserted, but existing ones are preserved.

Example:

Call it with managed-block’s locals() and tell which of them to salvage in case of errors:

try:
    a = 1
    b = 2
    raise Exception()
exception Exception as ex:
    jetsam(ex, locals(), "a", b="salvaged_b", c_var="c")

And then from a REPL:

import sys
sys.last_value.jetsam
{'a': 1, 'salvaged_b': 2, "c_var": None}

** Reason:**

Graphs may become arbitrary deep. Debugging such graphs is notoriously hard.

The purpose is not to require a debugger-session to inspect the root-causes (without precluding one).

Naively salvaging values with a simple try/except block around each function, blocks the debugger from landing on the real cause of the error - it would land on that block; and that could be many nested levels above it.